Working Papers
1.
Fang Wan, Chaoyang Song
The Bionic Design and Learning of Overconstrained Robotic Limbs Working paper Forthcoming
Forthcoming.
BibTeX | Tags: Authorship - Corresponding, Special - Working Paper
@workingpaper{Wan2024BionicDL,
title = {The Bionic Design and Learning of Overconstrained Robotic Limbs},
author = {Fang Wan and Chaoyang Song},
keywords = {Authorship - Corresponding, Special - Working Paper},
pubstate = {forthcoming},
tppubtype = {workingpaper}
}
Under Review
7.
Xi Xia, Xingxing Chen, Junli Shi, Zhibin Li, Bingfa Jiang, Kaixi Huang, Mengxue Guo, Zeyun Yang, Zelong Liao, Chaoyang Song, Chuanfei Guo
Microstructure-Enabled Tough Adhesion and Enhanced Sensing Online Forthcoming
Forthcoming, visited: 13.01.2025, (Submitted to Matter).
Abstract | BibTeX | Tags: Authorship - Co-Author, Status - Under Review
@online{Xia2025MicrostructureEnabled,
title = {Microstructure-Enabled Tough Adhesion and Enhanced Sensing},
author = {Xi Xia and Xingxing Chen and Junli Shi and Zhibin Li and Bingfa Jiang and Kaixi Huang and Mengxue Guo and Zeyun Yang and Zelong Liao and Chaoyang Song and Chuanfei Guo},
year = {2025},
date = {2025-01-13},
urldate = {2025-01-13},
abstract = {Skin-like soft sensors are a key technology for humanoid robots and wearables. Achieving both robust interfaces and promoted sensing performances in soft sensors may enable their applications in extreme mechanical conditions of high shear and large strain. However, introducing tough adhesion to the interfaces in a sensor often compromises its sensing properties. Here, we use micropillars of hyperbranched polyurethane with a diameter smaller than its length of flaw sensitivity serving as an adhesion layer for exceptional mechanical stability, and also as an adaptive spacer for enhanced sensing properties. We show a strong size effect of the structures to toughen the interface, with ultrahigh interfacial toughness up to 5095 J m-2 at a pillar diameter of 50 μm, which is one order of magnitude higher than the state-of-the-arts results. As a spacer, the micropillars provide enhanced sensitivity, adaptive limit of detection, rapid response to the acoustic range by decreasing the stiffness via elastic buckling. The sensors are ideal for the manipulation of heavy objects in humanoid robots and other applications. },
note = {Submitted to Matter},
keywords = {Authorship - Co-Author, Status - Under Review},
pubstate = {forthcoming},
tppubtype = {online}
}
Skin-like soft sensors are a key technology for humanoid robots and wearables. Achieving both robust interfaces and promoted sensing performances in soft sensors may enable their applications in extreme mechanical conditions of high shear and large strain. However, introducing tough adhesion to the interfaces in a sensor often compromises its sensing properties. Here, we use micropillars of hyperbranched polyurethane with a diameter smaller than its length of flaw sensitivity serving as an adhesion layer for exceptional mechanical stability, and also as an adaptive spacer for enhanced sensing properties. We show a strong size effect of the structures to toughen the interface, with ultrahigh interfacial toughness up to 5095 J m-2 at a pillar diameter of 50 μm, which is one order of magnitude higher than the state-of-the-arts results. As a spacer, the micropillars provide enhanced sensitivity, adaptive limit of detection, rapid response to the acoustic range by decreasing the stiffness via elastic buckling. The sensors are ideal for the manipulation of heavy objects in humanoid robots and other applications.
6.
Victor-Louis De Gusseme, Thomas Lips, Remko Proesmans, Julius Hietala, Giwan Lee, Jiyoung Choi, Jeongil Choi, Geon Kim, Phayuth Yonrith, Domen Tabernik, Andrej Gams, Peter Nimac, Matej Urbas, Jon Muhovic, Danijel Skocaj, Matija Mavsar, Hyojeong Yu, Minseo Kwon, Young J. Kim, Yang Cong, Ronghan Chen, Yu Ren, Supeng Diao, Jiawei Weng, Jiayue Liu, Haoran Sun, Linhan Yang, Zeqing Zhang, Ning Guo, Lei Yang, Fang Wan, Chaoyang Song, Jia Pan, Yixiang Jin, Yong A, Jun Shi, Dingzhe Li, Yong Yang, Kakeru Yamasaki, Takumi Kajiwara, Yuki Nakadera, Krati Saxena, Tomohiro Shibata, Chongkun Xia, Kai Mo, Yanzhao Yu, Qihao Lin, Binqiang Ma, Uihun Sagong, JungHyun Choi, JeongHyun Park, Dongwoo Lee, Yeongmin Kim, Myun Joong Hwang, Yusuke Kuribayashi, Naoki Hiratsuka, Daisuke Tanaka, Solvi Arnold, Kimitoshi Yamazaki, Carlos Mateo-Agullo, Andreas Verleysen, Francis wyffels
A Dataset and Benchmark for Robotic Cloth Unfolding Grasp Selection: The ICRA 2024 Cloth Competition Online Forthcoming
Forthcoming, visited: 10.01.2025, (Submitted to The International Journal of Robotics Research).
Abstract | Links | BibTeX | Tags: Authorship - Co-Author, Status - Under Review
@online{DeGusseme2024BenchmarkingGrasp,
title = {A Dataset and Benchmark for Robotic Cloth Unfolding Grasp Selection: The ICRA 2024 Cloth Competition},
author = {Victor-Louis De Gusseme and Thomas Lips and Remko Proesmans and Julius Hietala and Giwan Lee and Jiyoung Choi and Jeongil Choi and Geon Kim and Phayuth Yonrith and Domen Tabernik and Andrej Gams and Peter Nimac and Matej Urbas and Jon Muhovic and Danijel Skocaj and Matija Mavsar and Hyojeong Yu and Minseo Kwon and Young J. Kim and Yang Cong and Ronghan Chen and Yu Ren and Supeng Diao and Jiawei Weng and Jiayue Liu and Haoran Sun and Linhan Yang and Zeqing Zhang and Ning Guo and Lei Yang and Fang Wan and Chaoyang Song and Jia Pan and Yixiang Jin and Yong A and Jun Shi and Dingzhe Li and Yong Yang and Kakeru Yamasaki and Takumi Kajiwara and Yuki Nakadera and Krati Saxena and Tomohiro Shibata and Chongkun Xia and Kai Mo and Yanzhao Yu and Qihao Lin and Binqiang Ma and Uihun Sagong and JungHyun Choi and JeongHyun Park and Dongwoo Lee and Yeongmin Kim and Myun Joong Hwang and Yusuke Kuribayashi and Naoki Hiratsuka and Daisuke Tanaka and Solvi Arnold and Kimitoshi Yamazaki and Carlos Mateo-Agullo and Andreas Verleysen and Francis wyffels},
url = {https://airo.ugent.be/cloth_competition/},
year = {2025},
date = {2025-01-10},
urldate = {2025-01-10},
abstract = {Robotic cloth manipulation suffers from a lack of standardized benchmarks and shared datasets for evaluating and comparing different approaches. To address this, we organized the ICRA 2024 Cloth Competition, a unique head-to-head evaluation focused on grasp pose selection for cloth unfolding. Eleven diverse teams competed with a shared dual-arm robot, utilizing our publicly released dataset of real-world robotic cloth unfolding attempts. We expanded this dataset with 176 live evaluation trials, which now encompasses 679 unfolding demonstrations across 34 garments. The competition established a key benchmark and reference for robotic cloth manipulation. Analysis revealed a significant discrepancy between competition performance and prior work, underscoring the importance of independent out-of-the-lab evaluation in robotic cloth manipulation. The resulting dataset, one of the most comprehensive collections of real-world robotic cloth manipulation data, is a valuable resource for developing and evaluating grasp selection methods, particularly for learning-based approaches. It can serve as a foundation for future benchmarks and drive further progress in data-driven robotic cloth manipulation.},
note = {Submitted to The International Journal of Robotics Research},
keywords = {Authorship - Co-Author, Status - Under Review},
pubstate = {forthcoming},
tppubtype = {online}
}
Robotic cloth manipulation suffers from a lack of standardized benchmarks and shared datasets for evaluating and comparing different approaches. To address this, we organized the ICRA 2024 Cloth Competition, a unique head-to-head evaluation focused on grasp pose selection for cloth unfolding. Eleven diverse teams competed with a shared dual-arm robot, utilizing our publicly released dataset of real-world robotic cloth unfolding attempts. We expanded this dataset with 176 live evaluation trials, which now encompasses 679 unfolding demonstrations across 34 garments. The competition established a key benchmark and reference for robotic cloth manipulation. Analysis revealed a significant discrepancy between competition performance and prior work, underscoring the importance of independent out-of-the-lab evaluation in robotic cloth manipulation. The resulting dataset, one of the most comprehensive collections of real-world robotic cloth manipulation data, is a valuable resource for developing and evaluating grasp selection methods, particularly for learning-based approaches. It can serve as a foundation for future benchmarks and drive further progress in data-driven robotic cloth manipulation.
5.
Xudong Han, Haoran Sun, Ning Guo, Sheng Ge, Jia Pan, Fang Wan, Chaoyang Song
Transferrable Robot Skills Approaching Human-Level Versatility in Automated Task Board Manipulation Online Forthcoming
Forthcoming, visited: 15.12.2024, (Submitted to IEEE Robotics and Automation Practics for the Special Collection "Autonomous Robotic Grasping and Manipulation in Real-World Applications.").
Abstract | BibTeX | Tags: Authorship - Corresponding, Status - Under Review
@online{Han2025TransferrableRobot,
title = {Transferrable Robot Skills Approaching Human-Level Versatility in Automated Task Board Manipulation},
author = {Xudong Han and Haoran Sun and Ning Guo and Sheng Ge and Jia Pan and Fang Wan and Chaoyang Song},
year = {2024},
date = {2024-12-15},
urldate = {2024-12-15},
abstract = {Versatility in engineering means adaptability and multi-functionality. For robotic automation, it signifies the ability to handle diverse tasks, easily switch between different operations, and thrive in changing environments. The current gap lies in developing agreed-upon frameworks and metrics that are both quantitative and context-appropriate, capturing not just mechanical capabilities but also cognitive adaptability, integration complexity, and economic value.
In this paper, we present the Design and Learning Research Group's (DLRG) solution for the euROBIN Manipulation Skill Versatility Challenge (MSVC) at IROS 2024 in Abu Dhabi, UAE. The MSVC, held annually since 2021, is part of the euROBIN project that seeks to advance transferrable robot skills for the circular economy by autonomously performing tasks such as object localization, insertion, door operation, circuit probing, and cable management. We approached the standardized task board provided by event organizers that mimics industrial testing procedures by structurally decomposing the task into subtask skills. We created a custom dashboard with drag-and-drop code blocks to streamline development and adaptation, enabling rapid code refinement and task restructuring, complementing the default remote web platform that records the performance. Our system completed the task board in 28.2 sec in the lab (37.2 sec on-site), nearly tripling the efficiency over the averaged best time of 83.5 sec by previous teams and bringing performance closer to a human baseline of 16.3 sec. By implementing subtasks as reusable code blocks, we facilitated the transfer of these skills to a distinct scenario, successfully removing a battery from a smoke detector with minimal reconfiguration.
We also provide suggestions for future research and industrial practice on robotic versatility in manipulation automation through globalized competitions, interdisciplinary efforts, standardization initiatives, and iterative testing in the real world to ensure that it is measured in a meaningful, actionable way.},
note = {Submitted to IEEE Robotics and Automation Practics for the Special Collection "Autonomous Robotic Grasping and Manipulation in Real-World Applications."},
keywords = {Authorship - Corresponding, Status - Under Review},
pubstate = {forthcoming},
tppubtype = {online}
}
Versatility in engineering means adaptability and multi-functionality. For robotic automation, it signifies the ability to handle diverse tasks, easily switch between different operations, and thrive in changing environments. The current gap lies in developing agreed-upon frameworks and metrics that are both quantitative and context-appropriate, capturing not just mechanical capabilities but also cognitive adaptability, integration complexity, and economic value.
In this paper, we present the Design and Learning Research Group's (DLRG) solution for the euROBIN Manipulation Skill Versatility Challenge (MSVC) at IROS 2024 in Abu Dhabi, UAE. The MSVC, held annually since 2021, is part of the euROBIN project that seeks to advance transferrable robot skills for the circular economy by autonomously performing tasks such as object localization, insertion, door operation, circuit probing, and cable management. We approached the standardized task board provided by event organizers that mimics industrial testing procedures by structurally decomposing the task into subtask skills. We created a custom dashboard with drag-and-drop code blocks to streamline development and adaptation, enabling rapid code refinement and task restructuring, complementing the default remote web platform that records the performance. Our system completed the task board in 28.2 sec in the lab (37.2 sec on-site), nearly tripling the efficiency over the averaged best time of 83.5 sec by previous teams and bringing performance closer to a human baseline of 16.3 sec. By implementing subtasks as reusable code blocks, we facilitated the transfer of these skills to a distinct scenario, successfully removing a battery from a smoke detector with minimal reconfiguration.
We also provide suggestions for future research and industrial practice on robotic versatility in manipulation automation through globalized competitions, interdisciplinary efforts, standardization initiatives, and iterative testing in the real world to ensure that it is measured in a meaningful, actionable way.
In this paper, we present the Design and Learning Research Group's (DLRG) solution for the euROBIN Manipulation Skill Versatility Challenge (MSVC) at IROS 2024 in Abu Dhabi, UAE. The MSVC, held annually since 2021, is part of the euROBIN project that seeks to advance transferrable robot skills for the circular economy by autonomously performing tasks such as object localization, insertion, door operation, circuit probing, and cable management. We approached the standardized task board provided by event organizers that mimics industrial testing procedures by structurally decomposing the task into subtask skills. We created a custom dashboard with drag-and-drop code blocks to streamline development and adaptation, enabling rapid code refinement and task restructuring, complementing the default remote web platform that records the performance. Our system completed the task board in 28.2 sec in the lab (37.2 sec on-site), nearly tripling the efficiency over the averaged best time of 83.5 sec by previous teams and bringing performance closer to a human baseline of 16.3 sec. By implementing subtasks as reusable code blocks, we facilitated the transfer of these skills to a distinct scenario, successfully removing a battery from a smoke detector with minimal reconfiguration.
We also provide suggestions for future research and industrial practice on robotic versatility in manipulation automation through globalized competitions, interdisciplinary efforts, standardization initiatives, and iterative testing in the real world to ensure that it is measured in a meaningful, actionable way.
4.
Yuping Gu, Bangchao Huang, Haoran Sun, Ronghan Xu, Jiayi Yin, Wei Zhang, Fang Wan, Jia Pan, Chaoyang Song
One-DoF Robotic Design of Overconstrained Limbs with Energy-Efficient, Self-Collision-Free Motion Online Forthcoming
Forthcoming, (Submitted to Fundamental Research).
Abstract | BibTeX | Tags: Authorship - Corresponding, Status - Under Review
@online{Gu2024OCLimbDesign,
title = {One-DoF Robotic Design of Overconstrained Limbs with Energy-Efficient, Self-Collision-Free Motion},
author = {Yuping Gu and Bangchao Huang and Haoran Sun and Ronghan Xu and Jiayi Yin and Wei Zhang and Fang Wan and Jia Pan and Chaoyang Song},
year = {2024},
date = {2024-10-27},
abstract = {While it is common to build robotic limbs with multiple degrees of freedom (DoF) inspired by nature, single DoF design remains fundamental, providing benefits including, but not limited to, simplicity, robustness, cost-effectiveness, and efficiency. Mechanisms, especially those with multiple links and revolute joints connected in closed loops, play an enabling factor in introducing motion diversity for 1-DoF systems, which are usually constrained by self-collision during a full-cycle range of motion. This study presents a novel computational approach to designing 1-DoF overconstrained robotic limbs for desired spatial trajectory while achieving energy-efficient, self-collision-free motion in full-cycle rotations. Firstly, we present the geometric optimization problem of linkage-based robotic limbs in a generalized formulation for self-collision-free design. Next, we formulate the spatial trajectory generation problem with the overconstrained linkages by optimizing the similarity and dynamic-related metrics. We further optimize the geometric shape of the overconstrained linkage to ensure smooth and collision-free motion driven by a single actuator. We validated our proposed method through various experiments, including personalized automata and bio-inspired hexapod robots. The resulting hexapod robot with overconstrained robotic limbs showed outstanding energy efficiency in forward walking.},
note = {Submitted to Fundamental Research},
keywords = {Authorship - Corresponding, Status - Under Review},
pubstate = {forthcoming},
tppubtype = {online}
}
While it is common to build robotic limbs with multiple degrees of freedom (DoF) inspired by nature, single DoF design remains fundamental, providing benefits including, but not limited to, simplicity, robustness, cost-effectiveness, and efficiency. Mechanisms, especially those with multiple links and revolute joints connected in closed loops, play an enabling factor in introducing motion diversity for 1-DoF systems, which are usually constrained by self-collision during a full-cycle range of motion. This study presents a novel computational approach to designing 1-DoF overconstrained robotic limbs for desired spatial trajectory while achieving energy-efficient, self-collision-free motion in full-cycle rotations. Firstly, we present the geometric optimization problem of linkage-based robotic limbs in a generalized formulation for self-collision-free design. Next, we formulate the spatial trajectory generation problem with the overconstrained linkages by optimizing the similarity and dynamic-related metrics. We further optimize the geometric shape of the overconstrained linkage to ensure smooth and collision-free motion driven by a single actuator. We validated our proposed method through various experiments, including personalized automata and bio-inspired hexapod robots. The resulting hexapod robot with overconstrained robotic limbs showed outstanding energy efficiency in forward walking.
3.
Fang Wan, Zheng Wang, Wei Zhang, Chaoyang Song
SeeThruFinger: See and Grasp Anything via a Multi-Modal Soft Touch Online Forthcoming
Forthcoming, (Submitted to IEEE Transactions on Robotics).
Abstract | Links | BibTeX | Tags: Authorship - Corresponding, Status - Under Review
@online{Wan2024SeeThruFinger,
title = {SeeThruFinger: See and Grasp Anything via a Multi-Modal Soft Touch},
author = {Fang Wan and Zheng Wang and Wei Zhang and Chaoyang Song},
doi = {10.48550/arXiv.2312.09822},
year = {2024},
date = {2024-09-20},
abstract = {We present SeeThruFinger, a Vision-Based Tactile Sensing (VBTS) architecture using a markerless See-Thru-Network. It achieves simultaneous visual perception and tactile sensing while providing omni-directional, adaptive grasping for manipulation. Multi-modal perception of intrinsic and extrinsic interactions is critical in building intelligent robots that learn. Instead of adding various sensors for different modalities, a preferred solution is to integrate them into one elegant and coherent design, which is a challenging task. This study leverages the in-finger vision to inpaint occluded regions of the external environment, achieving coherent scene reconstruction for visual perception. By tracking real-time segmentation of the Soft Polyhedral Network’s large-scale deformation, we achieved real- time markerless tactile sensing of 6D forces and torques. We demonstrate the capable performances of the SeeThruFinger for reactive grasping without using external cameras or dedicated force and torque sensors on the fingertips. Using the inpainted scene and the deformation mask, we further demonstrate the multi-modal performance of the SeeThruFinger architecture to simultaneously achieve various capabilities, including but not limited to scene inpainting, object detection, depth sensing, scene segmentation, masked deformation tracking, 6D force-and-torque sensing, and contact event detection, all within a single input from the in-finger vision of the See-Thru-Network in a markerless way. All codes are available at https://github.com/ ancorasir/SeeThruFinger.},
note = {Submitted to IEEE Transactions on Robotics},
keywords = {Authorship - Corresponding, Status - Under Review},
pubstate = {forthcoming},
tppubtype = {online}
}
We present SeeThruFinger, a Vision-Based Tactile Sensing (VBTS) architecture using a markerless See-Thru-Network. It achieves simultaneous visual perception and tactile sensing while providing omni-directional, adaptive grasping for manipulation. Multi-modal perception of intrinsic and extrinsic interactions is critical in building intelligent robots that learn. Instead of adding various sensors for different modalities, a preferred solution is to integrate them into one elegant and coherent design, which is a challenging task. This study leverages the in-finger vision to inpaint occluded regions of the external environment, achieving coherent scene reconstruction for visual perception. By tracking real-time segmentation of the Soft Polyhedral Network’s large-scale deformation, we achieved real- time markerless tactile sensing of 6D forces and torques. We demonstrate the capable performances of the SeeThruFinger for reactive grasping without using external cameras or dedicated force and torque sensors on the fingertips. Using the inpainted scene and the deformation mask, we further demonstrate the multi-modal performance of the SeeThruFinger architecture to simultaneously achieve various capabilities, including but not limited to scene inpainting, object detection, depth sensing, scene segmentation, masked deformation tracking, 6D force-and-torque sensing, and contact event detection, all within a single input from the in-finger vision of the See-Thru-Network in a markerless way. All codes are available at https://github.com/ ancorasir/SeeThruFinger.
2.
Chengxiao Dong, Yu Pan, Xuanyi Dai, Edmond Ho Nang, Chaoyang Song, Fang Wan
Enhancing Full-Arch Intraoral Measurement with Robotic Process Automation Online Forthcoming
Forthcoming, (Submitted to Journal of Bionic Engineering).
Abstract | BibTeX | Tags: Authorship - Co-Author, Status - Under Review
@online{Dong2024EnhancingFull,
title = {Enhancing Full-Arch Intraoral Measurement with Robotic Process Automation},
author = {Chengxiao Dong and Yu Pan and Xuanyi Dai and Edmond Ho Nang and Chaoyang Song and Fang Wan},
year = {2024},
date = {2024-09-12},
abstract = {Intraoral scanning has become integral to digital workflows in dental implantology, offering a more efficient and comfortable alternative to conventional impression techniques. For complete edentulism, accurate scanning is crucial to successful full-arch dental implant rehabilitation. However, the absence of well-defined anatomical landmarks can lead to cumulative errors during merging sequential scans, often surpassing acceptable thresholds. Current mitigation strategies rely on manual adjustments in computer-aided design (CAD) software, a time-intensive process that depends heavily on the operator's expertise. This study presents a novel textit{segment-match-correct} robotic process automation (RPA) workflow to enhance full-arch intraoral scans' positioning accuracy and efficiency. By leveraging 3D registration algorithms, the proposed method improves implant positioning accuracy while significantly reducing manual labor. To assess the robustness of this workflow, we simulated four types of noise to evaluate their impact on scanning errors. Our findings demonstrate that the RPA workflow reduces dentist workload from 5-8 minutes per scan to less than 1 minute (about 57 seconds) while achieving a lower linear error of 45.16 $pm$ 23.76 unit{micrometer}, outperforming traditional scanning methods. We could replicate linear and angular deviations observed in real-world scans by simulating cumulative errors. This workflow improves the accuracy and efficiency of complete-arch implant rehabilitation and provides a practical solution to reduce cumulative scanning errors. Additionally, the noise simulations offer valuable insights into the origins of these errors, further optimizing intraoral scanner performance.},
note = {Submitted to Journal of Bionic Engineering},
keywords = {Authorship - Co-Author, Status - Under Review},
pubstate = {forthcoming},
tppubtype = {online}
}
Intraoral scanning has become integral to digital workflows in dental implantology, offering a more efficient and comfortable alternative to conventional impression techniques. For complete edentulism, accurate scanning is crucial to successful full-arch dental implant rehabilitation. However, the absence of well-defined anatomical landmarks can lead to cumulative errors during merging sequential scans, often surpassing acceptable thresholds. Current mitigation strategies rely on manual adjustments in computer-aided design (CAD) software, a time-intensive process that depends heavily on the operator's expertise. This study presents a novel textit{segment-match-correct} robotic process automation (RPA) workflow to enhance full-arch intraoral scans' positioning accuracy and efficiency. By leveraging 3D registration algorithms, the proposed method improves implant positioning accuracy while significantly reducing manual labor. To assess the robustness of this workflow, we simulated four types of noise to evaluate their impact on scanning errors. Our findings demonstrate that the RPA workflow reduces dentist workload from 5-8 minutes per scan to less than 1 minute (about 57 seconds) while achieving a lower linear error of 45.16 $pm$ 23.76 unit{micrometer}, outperforming traditional scanning methods. We could replicate linear and angular deviations observed in real-world scans by simulating cumulative errors. This workflow improves the accuracy and efficiency of complete-arch implant rehabilitation and provides a practical solution to reduce cumulative scanning errors. Additionally, the noise simulations offer valuable insights into the origins of these errors, further optimizing intraoral scanner performance.
1.
Rongzheng Zhang, Wanghongjie Qiu, Jianuo Qiu, Yuqin Guo, Chengxiao Dong, Tuo Zhang, Juan Yi, Chaoyang Song, Harry Asada, Fang Wan
Multi-Modal Intention Recognition Combining Head Motion and Throat Vibration for Underwater Superlimbs Online Forthcoming
Forthcoming, (Invited Submission to IEEE Transactions on Automation Science and Engineering).
Abstract | BibTeX | Tags: Authorship - Co-Author, Status - Under Review
@online{Zhang2024MultiModal,
title = {Multi-Modal Intention Recognition Combining Head Motion and Throat Vibration for Underwater Superlimbs},
author = {Rongzheng Zhang and Wanghongjie Qiu and Jianuo Qiu and Yuqin Guo and Chengxiao Dong and Tuo Zhang and Juan Yi and Chaoyang Song and Harry Asada and Fang Wan},
year = {2024},
date = {2024-09-01},
journal = {IEEE Transactions on Automation Science and Engineering},
abstract = {This paper presents a novel solution for underwater intention recognition that simultaneously detects head motion and throat vibration, enhancing multi-modal human-robot interactions for underwater diving. The system pairs with an underwater supernumerary robotic limb (superlimb), providing propulsion assistance to reduce the diver’s physical load and mental fatigue. An inertial measurement unit monitors head motion, while a throat microphone captures vocal vibrations. Learning algorithms process these signals to accurately interpret the diver’s intentions and map them to the superlimb for posture management. The system features a compact design optimized for diving scenarios and includes a multi-modal, real-time classification algorithm to distinguish various head motions and vocal signals. By collecting and analyzing underwater throat vibration data, the study demonstrates the feasibility of this approach, enabling continuous motion commands for enhanced diving assistance. The results show that the head motion recognition component of the system achieved a high classification accuracy of 94%, and throat vibration classification reached 86% accuracy on land and 89% underwater for various purposes.},
note = {Invited Submission to IEEE Transactions on Automation Science and Engineering},
keywords = {Authorship - Co-Author, Status - Under Review},
pubstate = {forthcoming},
tppubtype = {online}
}
This paper presents a novel solution for underwater intention recognition that simultaneously detects head motion and throat vibration, enhancing multi-modal human-robot interactions for underwater diving. The system pairs with an underwater supernumerary robotic limb (superlimb), providing propulsion assistance to reduce the diver’s physical load and mental fatigue. An inertial measurement unit monitors head motion, while a throat microphone captures vocal vibrations. Learning algorithms process these signals to accurately interpret the diver’s intentions and map them to the superlimb for posture management. The system features a compact design optimized for diving scenarios and includes a multi-modal, real-time classification algorithm to distinguish various head motions and vocal signals. By collecting and analyzing underwater throat vibration data, the study demonstrates the feasibility of this approach, enabling continuous motion commands for enhanced diving assistance. The results show that the head motion recognition component of the system achieved a high classification accuracy of 94%, and throat vibration classification reached 86% accuracy on land and 89% underwater for various purposes.
Journal Articles
38.
Yujian Dong, Tianyu Wu, Chaoyang Song
Optimizing Robotic Manipulation with Decision-RWKV: A Recurrent Sequence Modeling Approach for Lifelong Learning Journal Article
In: Journal of Computing and Information Science in Engineering, pp. JCISE-24-1245, 2024, (Accepted and Published Online).
Abstract | Links | BibTeX | Tags: Authorship - Corresponding, JCR Q2, Jour - J. Comput. Inf. Sci. Eng. (JCISE), Status - Accepted
@article{Dong2024OptimizingRobotic,
title = {Optimizing Robotic Manipulation with Decision-RWKV: A Recurrent Sequence Modeling Approach for Lifelong Learning},
author = {Yujian Dong and Tianyu Wu and Chaoyang Song},
url = {https://doi.org/10.48550/arXiv.2407.16306},
doi = {10.1115/1.4067524},
year = {2024},
date = {2024-12-23},
urldate = {2024-12-23},
journal = {Journal of Computing and Information Science in Engineering},
pages = {JCISE-24-1245},
abstract = {Models based on the Transformer architecture have seen widespread application across fields such as natural language processing (NLP), computer vision, and robotics, with large language models (LLMs) like ChatGPT revolutionizing machine understanding of human language and demonstrating impressive memory and reproduction capabilities. Traditional machine learning algorithms struggle with catastrophic forgetting, which is detrimental to the diverse and generalized abilities required for robotic deployment. This paper investigates the Receptance Weighted Key Value (RWKV) framework, known for its advanced capabilities in efficient and effective sequence modeling, integration with the decision transformer (DT), and experience replay architectures. It focuses on potential performance enhancements in sequence decision-making and lifelong robotic learning tasks. We introduce the Decision-RWKV (DRWKV) model and conduct extensive experiments using the D4RL database within the OpenAI Gym environment and on the D’Claw platform to assess the DRWKV model's performance in single-task tests and lifelong learning scenarios, showcasing its ability to handle multiple subtasks efficiently. The code for all algorithms, training, and image rendering in this study is open-sourced at https://github.com/ancorasir/DecisionRWKV. },
note = {Accepted and Published Online},
keywords = {Authorship - Corresponding, JCR Q2, Jour - J. Comput. Inf. Sci. Eng. (JCISE), Status - Accepted},
pubstate = {published},
tppubtype = {article}
}
Models based on the Transformer architecture have seen widespread application across fields such as natural language processing (NLP), computer vision, and robotics, with large language models (LLMs) like ChatGPT revolutionizing machine understanding of human language and demonstrating impressive memory and reproduction capabilities. Traditional machine learning algorithms struggle with catastrophic forgetting, which is detrimental to the diverse and generalized abilities required for robotic deployment. This paper investigates the Receptance Weighted Key Value (RWKV) framework, known for its advanced capabilities in efficient and effective sequence modeling, integration with the decision transformer (DT), and experience replay architectures. It focuses on potential performance enhancements in sequence decision-making and lifelong robotic learning tasks. We introduce the Decision-RWKV (DRWKV) model and conduct extensive experiments using the D4RL database within the OpenAI Gym environment and on the D’Claw platform to assess the DRWKV model's performance in single-task tests and lifelong learning scenarios, showcasing its ability to handle multiple subtasks efficiently. The code for all algorithms, training, and image rendering in this study is open-sourced at https://github.com/ancorasir/DecisionRWKV.
37.
Xiaobo Liu, Xudong Han, Wei Hong, Fang Wan, Chaoyang Song
Proprioceptive Learning with Soft Polyhedral Networks Journal Article
In: The International Journal of Robotics Research, vol. 43, no. 12, pp. 1916-1935, 2024.
Abstract | Links | BibTeX | Tags: Authorship - Corresponding, JCR Q1, Jour - Int. J. Robot. Res. (IJRR)
@article{Liu20242024ProprioceptiveLearning,
title = {Proprioceptive Learning with Soft Polyhedral Networks},
author = {Xiaobo Liu and Xudong Han and Wei Hong and Fang Wan and Chaoyang Song},
doi = {10.1177/02783649241238765},
year = {2024},
date = {2024-10-07},
urldate = {2024-03-13},
journal = {The International Journal of Robotics Research},
volume = {43},
number = {12},
pages = {1916-1935},
abstract = {Proprioception is the “sixth sense” that detects limb postures with motor neurons. It requires a natural integration between the musculoskeletal systems and sensory receptors, which is challenging among modern robots that aim for lightweight, adaptive, and sensitive designs at low costs in mechanical design and algorithmic computation. Here, we present the Soft Polyhedral Network with an embedded vision for physical interactions, capable of adaptive kinesthesia and viscoelastic proprioception by learning kinetic features. This design enables passive adaptations to omni-directional interactions, visually captured by a miniature high-speed motion-tracking system embedded inside for proprioceptive learning. The results show that the soft network can infer real-time 6D forces and torques with accuracies of 0.25/0.24/0.35 N and 0.025/0.034/0.006 Nm in dynamic interactions. We also incorporate viscoelasticity in proprioception during static adaptation by adding a creep and relaxation modifier to refine the predicted results. The proposed soft network combines simplicity in design, omni-adaptation, and proprioceptive sensing with high accuracy, making it a versatile solution for robotics at a low material cost with more than one million use cycles for tasks such as sensitive and competitive grasping and touch-based geometry reconstruction. This study offers new insights into vision-based proprioception for soft robots in adaptive grasping, soft manipulation, and human-robot interaction.},
keywords = {Authorship - Corresponding, JCR Q1, Jour - Int. J. Robot. Res. (IJRR)},
pubstate = {published},
tppubtype = {article}
}
Proprioception is the “sixth sense” that detects limb postures with motor neurons. It requires a natural integration between the musculoskeletal systems and sensory receptors, which is challenging among modern robots that aim for lightweight, adaptive, and sensitive designs at low costs in mechanical design and algorithmic computation. Here, we present the Soft Polyhedral Network with an embedded vision for physical interactions, capable of adaptive kinesthesia and viscoelastic proprioception by learning kinetic features. This design enables passive adaptations to omni-directional interactions, visually captured by a miniature high-speed motion-tracking system embedded inside for proprioceptive learning. The results show that the soft network can infer real-time 6D forces and torques with accuracies of 0.25/0.24/0.35 N and 0.025/0.034/0.006 Nm in dynamic interactions. We also incorporate viscoelasticity in proprioception during static adaptation by adding a creep and relaxation modifier to refine the predicted results. The proposed soft network combines simplicity in design, omni-adaptation, and proprioceptive sensing with high accuracy, making it a versatile solution for robotics at a low material cost with more than one million use cycles for tasks such as sensitive and competitive grasping and touch-based geometry reconstruction. This study offers new insights into vision-based proprioception for soft robots in adaptive grasping, soft manipulation, and human-robot interaction.
36.
Xudong Han, Ning Guo, Yu Jie, He Wang, Fang Wan, Chaoyang Song
On Flange-Based 3D Hand-Eye Calibration for Soft Robotic Tactile Welding Journal Article
In: Measurement, vol. 238, iss. October, pp. 115376, 2024.
Abstract | Links | BibTeX | Tags: Authorship - Corresponding, JCR Q1, Jour - Measurement (MEAS)
@article{Han2024OnFlange,
title = {On Flange-Based 3D Hand-Eye Calibration for Soft Robotic Tactile Welding},
author = {Xudong Han and Ning Guo and Yu Jie and He Wang and Fang Wan and Chaoyang Song},
doi = {10.1016/j.measurement.2024.115376},
year = {2024},
date = {2024-10-01},
urldate = {2024-10-01},
journal = {Measurement},
volume = {238},
issue = {October},
pages = {115376},
abstract = {This paper investigates the direct application of standardized designs on the robot for conducting robot hand–eye calibration by employing 3D scanners with collaborative robots. The well-established geometric features of the robot flange are exploited by directly capturing its point cloud data. In particular, an iterative method is proposed to facilitate point cloud processing towards a refined calibration outcome. Several extensive experiments are conducted over a range of collaborative robots, including Universal Robots UR5 & UR10 e-series, Franka Emika, and AUBO i5 using an industrial-grade 3D scanner Photoneo Phoxi S & M and a commercial-grade 3D scanner Microsoft Azure Kinect DK. Experimental results show that translational and rotational errors converge efficiently to less than 0.28 mm and 0.25 degrees, respectively, achieving a hand–eye calibration accuracy as high as the camera’s resolution, probing the hardware limit. A welding seam tracking system is presented, combining the flange-based calibration method with soft tactile sensing. The experiment results show that the system enables the robot to adjust its motion in real-time, ensuring consistent weld quality and paving the way for more efficient and adaptable manufacturing processes.},
keywords = {Authorship - Corresponding, JCR Q1, Jour - Measurement (MEAS)},
pubstate = {published},
tppubtype = {article}
}
This paper investigates the direct application of standardized designs on the robot for conducting robot hand–eye calibration by employing 3D scanners with collaborative robots. The well-established geometric features of the robot flange are exploited by directly capturing its point cloud data. In particular, an iterative method is proposed to facilitate point cloud processing towards a refined calibration outcome. Several extensive experiments are conducted over a range of collaborative robots, including Universal Robots UR5 & UR10 e-series, Franka Emika, and AUBO i5 using an industrial-grade 3D scanner Photoneo Phoxi S & M and a commercial-grade 3D scanner Microsoft Azure Kinect DK. Experimental results show that translational and rotational errors converge efficiently to less than 0.28 mm and 0.25 degrees, respectively, achieving a hand–eye calibration accuracy as high as the camera’s resolution, probing the hardware limit. A welding seam tracking system is presented, combining the flange-based calibration method with soft tactile sensing. The experiment results show that the system enables the robot to adjust its motion in real-time, ensuring consistent weld quality and paving the way for more efficient and adaptable manufacturing processes.
35.
Ning Guo, Xudong Han, Shuqiao Zhong, Zhiyuan Zhou, Jian Lin, Fang Wan, Chaoyang Song
Reconstructing Soft Robotic Touch via In-Finger Vision Journal Article
In: Advanced Intelligent Systems, vol. 6, iss. October, no. 10, pp. 2400022, 2024.
Abstract | Links | BibTeX | Tags: Authorship - Corresponding, Award - Front Cover, JCR Q1, Jour - Adv. Intell. Syst. (AIS)
@article{Guo2024ReconstructingSoft,
title = {Reconstructing Soft Robotic Touch via In-Finger Vision},
author = {Ning Guo and Xudong Han and Shuqiao Zhong and Zhiyuan Zhou and Jian Lin and Fang Wan and Chaoyang Song},
doi = {10.1002/aisy.202400022},
year = {2024},
date = {2024-10-01},
urldate = {2024-10-01},
journal = {Advanced Intelligent Systems},
volume = {6},
number = {10},
issue = {October},
pages = {2400022},
abstract = {Incorporating authentic tactile interactions into virtual environments presents a notable challenge for the emerging development of soft robotic metamaterials. In this study, a vision-based approach is introduced to learning proprioceptive interactions by simultaneously reconstructing the shape and touch of a soft robotic metamaterial (SRM) during physical engagements. The SRM design is optimized to the size of a finger with enhanced adaptability in 3D interactions while incorporating a see-through viewing field inside, which can be visually captured by a miniature camera underneath to provide a rich set of image features for touch digitization. Employing constrained geometric optimization, the proprioceptive process with aggregated multi-handles is modeled. This approach facilitates real-time, precise, and realistic estimations of the finger's mesh deformation within a virtual environment. Herein, a data-driven learning model is also proposed to estimate touch positions, achieving reliable results with impressive R2 scores of 0.9681, 0.9415, and 0.9541 along the x, y, and z axes. Furthermore, the robust performance of the proposed methods in touch-based human–cybernetic interfaces and human–robot collaborative grasping is demonstrated. In this study, the door is opened to future applications in touch-based digital twin interactions through vision-based soft proprioception.},
keywords = {Authorship - Corresponding, Award - Front Cover, JCR Q1, Jour - Adv. Intell. Syst. (AIS)},
pubstate = {published},
tppubtype = {article}
}
Incorporating authentic tactile interactions into virtual environments presents a notable challenge for the emerging development of soft robotic metamaterials. In this study, a vision-based approach is introduced to learning proprioceptive interactions by simultaneously reconstructing the shape and touch of a soft robotic metamaterial (SRM) during physical engagements. The SRM design is optimized to the size of a finger with enhanced adaptability in 3D interactions while incorporating a see-through viewing field inside, which can be visually captured by a miniature camera underneath to provide a rich set of image features for touch digitization. Employing constrained geometric optimization, the proprioceptive process with aggregated multi-handles is modeled. This approach facilitates real-time, precise, and realistic estimations of the finger's mesh deformation within a virtual environment. Herein, a data-driven learning model is also proposed to estimate touch positions, achieving reliable results with impressive R2 scores of 0.9681, 0.9415, and 0.9541 along the x, y, and z axes. Furthermore, the robust performance of the proposed methods in touch-based human–cybernetic interfaces and human–robot collaborative grasping is demonstrated. In this study, the door is opened to future applications in touch-based digital twin interactions through vision-based soft proprioception.
34.
Ning Guo, Xudong Han, Shuqiao Zhong, Zhiyuan Zhou, Jian Lin, Jiansheng Dai, Fang Wan, Chaoyang Song
Proprioceptive State Estimation for Amphibious Tactile Sensing Journal Article
In: IEEE Transactions on Robotics, vol. 40, iss. September, pp. 4684-4698, 2024.
Abstract | Links | BibTeX | Tags: Authorship - Corresponding, JCR Q1, Jour - IEEE Trans. Robot. (T-RO)
@article{Guo2024ProprioceptiveState,
title = {Proprioceptive State Estimation for Amphibious Tactile Sensing},
author = {Ning Guo and Xudong Han and Shuqiao Zhong and Zhiyuan Zhou and Jian Lin and Jiansheng Dai and Fang Wan and Chaoyang Song},
doi = {10.1109/TRO.2024.3463509},
year = {2024},
date = {2024-09-18},
urldate = {2024-09-18},
journal = {IEEE Transactions on Robotics},
volume = {40},
issue = {September},
pages = {4684-4698},
abstract = {This article presents a novel vision-based proprioception approach for a soft robotic finger that can estimate and reconstruct tactile interactions in terrestrial and aquatic environments. The key to this system lies in the finger's unique metamaterial structure, which facilitates omnidirectional passive adaptation during grasping, protecting delicate objects across diverse scenarios. A compact in-finger camera captures high-framerate images of the finger's deformation during contact, extracting crucial tactile data in real time. We present a volumetric discretized model of the soft finger and use the geometry constraints captured by the camera to find the optimal estimation of the deformed shape. The approach is benchmarked using a motion capture system with sparse markers and a haptic device with dense measurements. Both results show state-of-the-art accuracy, with a median error of 1.96 mm for overall body deformation, corresponding to 2.1 % of the finger's length. More importantly, the state estimation is robust in both on-land and underwater environments as we demonstrate its usage for underwater object shape sensing. This combination of passive adaptation and real-time tactile sensing paves the way for amphibious robotic grasping applications.},
key = {2024-J-TRO-ProprioceptiveState},
keywords = {Authorship - Corresponding, JCR Q1, Jour - IEEE Trans. Robot. (T-RO)},
pubstate = {published},
tppubtype = {article}
}
This article presents a novel vision-based proprioception approach for a soft robotic finger that can estimate and reconstruct tactile interactions in terrestrial and aquatic environments. The key to this system lies in the finger's unique metamaterial structure, which facilitates omnidirectional passive adaptation during grasping, protecting delicate objects across diverse scenarios. A compact in-finger camera captures high-framerate images of the finger's deformation during contact, extracting crucial tactile data in real time. We present a volumetric discretized model of the soft finger and use the geometry constraints captured by the camera to find the optimal estimation of the deformed shape. The approach is benchmarked using a motion capture system with sparse markers and a haptic device with dense measurements. Both results show state-of-the-art accuracy, with a median error of 1.96 mm for overall body deformation, corresponding to 2.1 % of the finger's length. More importantly, the state estimation is robust in both on-land and underwater environments as we demonstrate its usage for underwater object shape sensing. This combination of passive adaptation and real-time tactile sensing paves the way for amphibious robotic grasping applications.
33.
Tianyu Wu, Yujian Dong, Xiaobo Liu, Xudong Han, Yang Xiao, Jinqi Wei, Fang Wan, Chaoyang Song
Vision-based Tactile Intelligence with Soft Robotic Metamaterial Journal Article
In: Materials & Design, vol. 238, iss. February, pp. 112629, 2024.
Abstract | Links | BibTeX | Tags: Authorship - Corresponding, JCR Q1, Jour - Mat. Des. (MADE)
@article{Wu2024VisionBasedSRM,
title = {Vision-based Tactile Intelligence with Soft Robotic Metamaterial},
author = {Tianyu Wu and Yujian Dong and Xiaobo Liu and Xudong Han and Yang Xiao and Jinqi Wei and Fang Wan and Chaoyang Song},
doi = {10.1016/j.matdes.2024.112629},
year = {2024},
date = {2024-02-01},
urldate = {2024-02-01},
booktitle = {IEEE International Conference on Advanced Robotics and Mechatronics (ICARM2024)},
journal = {Materials & Design},
volume = {238},
issue = {February},
pages = {112629},
abstract = {Robotic metamaterials represent an innovative approach to creating synthetic structures that combine desired material characteristics with embodied intelligence, blurring the boundaries between materials and machinery. Inspired by the functional qualities of biological skin, integrating tactile intelligence into these materials has gained significant interest for research and practical applications. This study introduces a Soft Robotic Metamaterial (SRM) design featuring omnidirectional adaptability and superior tactile sensing, combining vision-based motion tracking and machine learning. The study compares two sensory integration methods to a state-of-the-art motion tracking system and force/torque sensor baseline: an internal-vision design with high frame rates and an external-vision design offering cost-effectiveness. The results demonstrate the internal-vision SRM design achieving an impressive tactile accuracy of 98.96%, enabling soft and adaptive tactile interactions, especially beneficial for dexterous robotic grasping. The external-vision design offers similar performance at a reduced cost and can be adapted for portability, enhancing material science education and robotic learning. This research significantly advances tactile sensing using vision-based motion tracking in soft robotic metamaterials, and the open-source availability on GitHub fosters collaboration and further exploration of this innovative technology (https://github.com/bionicdl-sustech/SoftRoboticTongs).},
keywords = {Authorship - Corresponding, JCR Q1, Jour - Mat. Des. (MADE)},
pubstate = {published},
tppubtype = {article}
}
Robotic metamaterials represent an innovative approach to creating synthetic structures that combine desired material characteristics with embodied intelligence, blurring the boundaries between materials and machinery. Inspired by the functional qualities of biological skin, integrating tactile intelligence into these materials has gained significant interest for research and practical applications. This study introduces a Soft Robotic Metamaterial (SRM) design featuring omnidirectional adaptability and superior tactile sensing, combining vision-based motion tracking and machine learning. The study compares two sensory integration methods to a state-of-the-art motion tracking system and force/torque sensor baseline: an internal-vision design with high frame rates and an external-vision design offering cost-effectiveness. The results demonstrate the internal-vision SRM design achieving an impressive tactile accuracy of 98.96%, enabling soft and adaptive tactile interactions, especially beneficial for dexterous robotic grasping. The external-vision design offers similar performance at a reduced cost and can be adapted for portability, enhancing material science education and robotic learning. This research significantly advances tactile sensing using vision-based motion tracking in soft robotic metamaterials, and the open-source availability on GitHub fosters collaboration and further exploration of this innovative technology (https://github.com/bionicdl-sustech/SoftRoboticTongs).
32.
Ning Guo, Xudong Han, Xiaobo Liu, Shuqiao Zhong, Zhiyuan Zhou, Jian Lin, Jiansheng Dai, Fang Wan, Chaoyang Song
Autoencoding a Soft Touch to Learn Grasping from On-land to Underwater Journal Article
In: Advanced Intelligent Systems, vol. 6, iss. January, no. 1, pp. 2300382, 2024.
Abstract | Links | BibTeX | Tags: Authorship - Corresponding, Award - Front Cover, JCR Q1, Jour - Adv. Intell. Syst. (AIS)
@article{Guo2024AutoencodingA,
title = {Autoencoding a Soft Touch to Learn Grasping from On-land to Underwater},
author = {Ning Guo and Xudong Han and Xiaobo Liu and Shuqiao Zhong and Zhiyuan Zhou and Jian Lin and Jiansheng Dai and Fang Wan and Chaoyang Song},
doi = {10.1002/aisy.202300382},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Advanced Intelligent Systems},
volume = {6},
number = {1},
issue = {January},
pages = {2300382},
abstract = {Robots play a critical role as the physical agent of human operators in exploring the ocean. However, it remains challenging to grasp objects reliably while fully submerging under a highly pressurized aquatic environment with little visible light, mainly due to the fluidic interference on the tactile mechanics between the finger and object surfaces. This study investigates the transferability of grasping knowledge from on-land to underwater via a vision-based soft robotic finger that learns 6D forces and torques (FT) using a supervised variational autoencoder (SVAE). A high-framerate camera captures the whole-body deformations while a soft robotic finger interacts with physical objects on-land and underwater. Results show that the trained SVAE model learns a series of latent representations of the soft mechanics transferable from land to water, presenting a superior adaptation to the changing environments against commercial FT sensors. Soft, delicate, and reactive grasping enabled by tactile intelligence enhances the gripper's underwater interaction with improved reliability and robustness at a much-reduced cost, paving the path for learning-based intelligent grasping to support fundamental scientific discoveries in environmental and ocean research.},
keywords = {Authorship - Corresponding, Award - Front Cover, JCR Q1, Jour - Adv. Intell. Syst. (AIS)},
pubstate = {published},
tppubtype = {article}
}
Robots play a critical role as the physical agent of human operators in exploring the ocean. However, it remains challenging to grasp objects reliably while fully submerging under a highly pressurized aquatic environment with little visible light, mainly due to the fluidic interference on the tactile mechanics between the finger and object surfaces. This study investigates the transferability of grasping knowledge from on-land to underwater via a vision-based soft robotic finger that learns 6D forces and torques (FT) using a supervised variational autoencoder (SVAE). A high-framerate camera captures the whole-body deformations while a soft robotic finger interacts with physical objects on-land and underwater. Results show that the trained SVAE model learns a series of latent representations of the soft mechanics transferable from land to water, presenting a superior adaptation to the changing environments against commercial FT sensors. Soft, delicate, and reactive grasping enabled by tactile intelligence enhances the gripper's underwater interaction with improved reliability and robustness at a much-reduced cost, paving the path for learning-based intelligent grasping to support fundamental scientific discoveries in environmental and ocean research.
31.
Yu Pan, Xuanyi Dai, Fang Wan, Chaoyang Song, James KH Tsoi, Edmond HN Pow
A Novel Post-Processing Strategy to Improve the Accuracy of Complete-Arch Intraoral Scanning for Implants: An In Vitro Study Journal Article
In: Journal of Dentistry, vol. 139, iss. December, pp. 104761, 2023.
Abstract | Links | BibTeX | Tags: Authorship - Co-Author, JCR Q1, Jour - J. Dent. (JoD)
@article{Pan2023ANovel,
title = {A Novel Post-Processing Strategy to Improve the Accuracy of Complete-Arch Intraoral Scanning for Implants: An In Vitro Study},
author = {Yu Pan and Xuanyi Dai and Fang Wan and Chaoyang Song and James KH Tsoi and Edmond HN Pow},
doi = {10.1016/j.jdent.2023.104761},
year = {2023},
date = {2023-12-01},
urldate = {2023-12-01},
journal = {Journal of Dentistry},
volume = {139},
issue = {December},
pages = {104761},
abstract = {[Objectives] To develop a new post-processing strategy that utilizes an auxiliary device to adjust intraoral scans and improve the accuracy of 3D models of complete-arch dental implants.
[Materials and methods] An edentulous resin model with 6 dental implants was prepared. An auxiliary device, consisting of an opaque base and artificial landmarks, was fabricated and mounted onto the resin model. Twenty intraoral scans (raw scans) were taken using this setup. A new post-processing strategy was proposed to adjust the raw scans using reverse engineering software (verified group). Additionally, ten conventional gypsum casts were duplicated and digitized using a laboratory scanner. The linear and angular trueness and precision of the models were evaluated and compared. The effect of the proposed strategy on the accuracy of complete-arch intraoral scans was analyzed using one-way ANOVA.
[Results] The linear trueness (29.7 µm) and precision (24.8 µm) of the verified group were significantly better than the raw scans (46.6 µm, 44.7 µm) and conventional casts (51.3 µm, 36.5 µm), particularly in cross-arch sites. However, the angular trueness (0.114°) and precision (0.085°) of the conventional casts were significantly better than both the verified models (0.298°, 0.168°) and the raw scans (0.288°, 0.202°).
[Conclusions] The novel post-processing strategy is effective in enhancing the linear accuracy of complete-arch implant IO scans, especially in cross-arch sites. However, further improvement is needed to eliminate the angular deviations.
[Clinical significance] Errors generated from intraoral scanning in complete edentulous arches exceed the clinical threshold. The elimination of stitching errors in the raw scans particularly in the cross-arch sites, through the proposed post-processing strategy would enhance the accuracy of complete-arch implant prostheses.},
keywords = {Authorship - Co-Author, JCR Q1, Jour - J. Dent. (JoD)},
pubstate = {published},
tppubtype = {article}
}
[Objectives] To develop a new post-processing strategy that utilizes an auxiliary device to adjust intraoral scans and improve the accuracy of 3D models of complete-arch dental implants.
[Materials and methods] An edentulous resin model with 6 dental implants was prepared. An auxiliary device, consisting of an opaque base and artificial landmarks, was fabricated and mounted onto the resin model. Twenty intraoral scans (raw scans) were taken using this setup. A new post-processing strategy was proposed to adjust the raw scans using reverse engineering software (verified group). Additionally, ten conventional gypsum casts were duplicated and digitized using a laboratory scanner. The linear and angular trueness and precision of the models were evaluated and compared. The effect of the proposed strategy on the accuracy of complete-arch intraoral scans was analyzed using one-way ANOVA.
[Results] The linear trueness (29.7 µm) and precision (24.8 µm) of the verified group were significantly better than the raw scans (46.6 µm, 44.7 µm) and conventional casts (51.3 µm, 36.5 µm), particularly in cross-arch sites. However, the angular trueness (0.114°) and precision (0.085°) of the conventional casts were significantly better than both the verified models (0.298°, 0.168°) and the raw scans (0.288°, 0.202°).
[Conclusions] The novel post-processing strategy is effective in enhancing the linear accuracy of complete-arch implant IO scans, especially in cross-arch sites. However, further improvement is needed to eliminate the angular deviations.
[Clinical significance] Errors generated from intraoral scanning in complete edentulous arches exceed the clinical threshold. The elimination of stitching errors in the raw scans particularly in the cross-arch sites, through the proposed post-processing strategy would enhance the accuracy of complete-arch implant prostheses.
[Materials and methods] An edentulous resin model with 6 dental implants was prepared. An auxiliary device, consisting of an opaque base and artificial landmarks, was fabricated and mounted onto the resin model. Twenty intraoral scans (raw scans) were taken using this setup. A new post-processing strategy was proposed to adjust the raw scans using reverse engineering software (verified group). Additionally, ten conventional gypsum casts were duplicated and digitized using a laboratory scanner. The linear and angular trueness and precision of the models were evaluated and compared. The effect of the proposed strategy on the accuracy of complete-arch intraoral scans was analyzed using one-way ANOVA.
[Results] The linear trueness (29.7 µm) and precision (24.8 µm) of the verified group were significantly better than the raw scans (46.6 µm, 44.7 µm) and conventional casts (51.3 µm, 36.5 µm), particularly in cross-arch sites. However, the angular trueness (0.114°) and precision (0.085°) of the conventional casts were significantly better than both the verified models (0.298°, 0.168°) and the raw scans (0.288°, 0.202°).
[Conclusions] The novel post-processing strategy is effective in enhancing the linear accuracy of complete-arch implant IO scans, especially in cross-arch sites. However, further improvement is needed to eliminate the angular deviations.
[Clinical significance] Errors generated from intraoral scanning in complete edentulous arches exceed the clinical threshold. The elimination of stitching errors in the raw scans particularly in the cross-arch sites, through the proposed post-processing strategy would enhance the accuracy of complete-arch implant prostheses.
30.
Xiaobo Liu, Xudong Han, Ning Guo, Fang Wan, Chaoyang Song
Bio-inspired Proprioceptive Touch of a Soft Finger with Inner-Finger Kinesthetic Perception Journal Article
In: Biomimetics, vol. 8, no. 6, pp. 501, 2023.
Abstract | Links | BibTeX | Tags: Authorship - Corresponding, JCR Q1, Jour - Biomimetics (Biomimetics)
@article{Liu2023BioInspired,
title = {Bio-inspired Proprioceptive Touch of a Soft Finger with Inner-Finger Kinesthetic Perception},
author = {Xiaobo Liu and Xudong Han and Ning Guo and Fang Wan and Chaoyang Song},
doi = {10.3390/biomimetics8060501},
year = {2023},
date = {2023-10-21},
urldate = {2023-10-21},
journal = {Biomimetics},
volume = {8},
number = {6},
pages = {501},
abstract = {In-hand object pose estimation is challenging for humans and robots due to occlusion caused by the hand and object. This paper proposes a soft finger that integrates inner vision with kinesthetic sensing to estimate object pose inspired by human fingers. The soft finger has a flexible skeleton and skin that adapts to different objects, and the skeleton deformations during interaction provide contact information obtained by the image from the inner camera. The proposed framework is an end-to-end method that uses raw images from soft fingers to estimate in-hand object pose. It consists of an encoder for kinesthetic information processing and an object pose and category estimator. The framework was tested on seven objects, achieving an impressive error of 2.02 mm and 11.34 degrees for pose error and 99.05% for classification.},
keywords = {Authorship - Corresponding, JCR Q1, Jour - Biomimetics (Biomimetics)},
pubstate = {published},
tppubtype = {article}
}
In-hand object pose estimation is challenging for humans and robots due to occlusion caused by the hand and object. This paper proposes a soft finger that integrates inner vision with kinesthetic sensing to estimate object pose inspired by human fingers. The soft finger has a flexible skeleton and skin that adapts to different objects, and the skeleton deformations during interaction provide contact information obtained by the image from the inner camera. The proposed framework is an end-to-end method that uses raw images from soft fingers to estimate in-hand object pose. It consists of an encoder for kinesthetic information processing and an object pose and category estimator. The framework was tested on seven objects, achieving an impressive error of 2.02 mm and 11.34 degrees for pose error and 99.05% for classification.
29.
Yuping Gu, Ziqian Wang, Shihao Feng, Haoran Sun, Haibo Lu, Jia Pan, Fang Wan, Chaoyang Song
Computational Design Towards Energy Efficient Optimization in Overconstrained Robotic Limbs Journal Article
In: Journal of Computational Design and Engineering, vol. 10, iss. October, no. 5, pp. 1941–1956, 2023.
Abstract | Links | BibTeX | Tags: Authorship - Corresponding, Award - Editor's Choice, JCR Q1, Jour - J. Comput. Des. Eng. (JCDE)
@article{Gu2023ComputationalDesign,
title = {Computational Design Towards Energy Efficient Optimization in Overconstrained Robotic Limbs},
author = {Yuping Gu and Ziqian Wang and Shihao Feng and Haoran Sun and Haibo Lu and Jia Pan and Fang Wan and Chaoyang Song},
doi = {10.1093/jcde/qwad083},
year = {2023},
date = {2023-08-22},
urldate = {2023-08-22},
journal = {Journal of Computational Design and Engineering},
volume = {10},
number = {5},
issue = {October},
pages = {1941–1956},
abstract = {Legged robots are constantly evolving, and energy efficiency is a major driving factor in their design. However, combining mechanism efficiency and trajectory planning can be challenging. This work proposes a computational optimization framework for optimizing leg design during basic walking while maximizing energy efficiency. We generalize the robotic limb design as a four-bar linkage-based design pool and optimize the leg using an evolutionary algorithm. The leg configuration and design parameters are optimized based on user-defined objective functions. Our framework was validated by comparing it to measured data on our prototype quadruped robot for forward trotting. The Bennett robotic leg was advantageous for omni-directional locomotion with enhanced energy efficiency.},
keywords = {Authorship - Corresponding, Award - Editor's Choice, JCR Q1, Jour - J. Comput. Des. Eng. (JCDE)},
pubstate = {published},
tppubtype = {article}
}
Legged robots are constantly evolving, and energy efficiency is a major driving factor in their design. However, combining mechanism efficiency and trajectory planning can be challenging. This work proposes a computational optimization framework for optimizing leg design during basic walking while maximizing energy efficiency. We generalize the robotic limb design as a four-bar linkage-based design pool and optimize the leg using an evolutionary algorithm. The leg configuration and design parameters are optimized based on user-defined objective functions. Our framework was validated by comparing it to measured data on our prototype quadruped robot for forward trotting. The Bennett robotic leg was advantageous for omni-directional locomotion with enhanced energy efficiency.
28.
Jiayu Huo, Jingran Wang, Yuqin Guo, Wanghongjie Qiu, Mingdong Chen, Harry Asada, Fang Wan, Chaoyang Song
Reconfigurable Design and Modeling of an Underwater Superlimb for Diving Assistance Journal Article
In: Advanced Intelligent Systems, vol. 5, iss. November, no. 11, pp. 2300245, 2023.
Abstract | Links | BibTeX | Tags: Authorship - Corresponding, Award - Back Cover, Award - Editor's Choice, JCR Q1, Jour - Adv. Intell. Syst. (AIS)
@article{Huo2023ReconfigurableDesign,
title = {Reconfigurable Design and Modeling of an Underwater Superlimb for Diving Assistance},
author = {Jiayu Huo and Jingran Wang and Yuqin Guo and Wanghongjie Qiu and Mingdong Chen and Harry Asada and Fang Wan and Chaoyang Song},
doi = {10.1002/aisy.202300245},
year = {2023},
date = {2023-08-17},
urldate = {2023-08-17},
journal = {Advanced Intelligent Systems},
volume = {5},
number = {11},
issue = {November},
pages = {2300245},
abstract = {This study presents the design of an underwater superlimb as a wearable robot, providing divers with mobility assistance and freeing their hands for manipulating tools underwater. The wearable design features a thrust vectoring system with two 3D-printed, waterproofed modules. The module with adjustable connections and strapping holes is designed to enable reconfiguration for multiple purposes, including regular use as an underwater superlimb for divers, manually operated as a handheld glider for swimmers, combined with an amphibian, legged robot as a quadruped superlimb, and coupled as a dual-unit autonomous underwater vehicle for underwater navigation. The kinematics and dynamics of the prototype and all of its reconfigured modes are developed. A sliding-mode controller is also introduced to achieve stable simulation in PyBullet. Field tests further support the feasibility of the underwater superlimb when worn on a test diver in a swimming pool. As the first underwater superlimb presented in the literature, this study opens new doors for supernumerary robotic limbs in underwater scenarios with multifunctional reconfiguration.},
keywords = {Authorship - Corresponding, Award - Back Cover, Award - Editor's Choice, JCR Q1, Jour - Adv. Intell. Syst. (AIS)},
pubstate = {published},
tppubtype = {article}
}
This study presents the design of an underwater superlimb as a wearable robot, providing divers with mobility assistance and freeing their hands for manipulating tools underwater. The wearable design features a thrust vectoring system with two 3D-printed, waterproofed modules. The module with adjustable connections and strapping holes is designed to enable reconfiguration for multiple purposes, including regular use as an underwater superlimb for divers, manually operated as a handheld glider for swimmers, combined with an amphibian, legged robot as a quadruped superlimb, and coupled as a dual-unit autonomous underwater vehicle for underwater navigation. The kinematics and dynamics of the prototype and all of its reconfigured modes are developed. A sliding-mode controller is also introduced to achieve stable simulation in PyBullet. Field tests further support the feasibility of the underwater superlimb when worn on a test diver in a swimming pool. As the first underwater superlimb presented in the literature, this study opens new doors for supernumerary robotic limbs in underwater scenarios with multifunctional reconfiguration.
27.
Haoran Sun, Linhan Yang, Yuping Gu, Jia Pan, Fang Wan, Chaoyang Song
Bridging Locomotion and Manipulation Using Reconfigurable Robotic Limbs via Reinforcement Learning Journal Article
In: Biomimetics, vol. 8, no. 4, pp. 364, 2023.
Abstract | Links | BibTeX | Tags: Authorship - Corresponding, JCR Q1, Jour - Biomimetics (Biomimetics)
@article{Sun2023BridgingLocomotion,
title = {Bridging Locomotion and Manipulation Using Reconfigurable Robotic Limbs via Reinforcement Learning},
author = {Haoran Sun and Linhan Yang and Yuping Gu and Jia Pan and Fang Wan and Chaoyang Song},
doi = {10.3390/biomimetics8040364},
year = {2023},
date = {2023-08-14},
urldate = {2023-08-14},
journal = {Biomimetics},
volume = {8},
number = {4},
pages = {364},
abstract = {Locomotion and manipulation are two essential skills in robotics but are often divided or decoupled into two separate problems. It is widely accepted that the topological duality between multi-legged locomotion and multi-fingered manipulation shares an intrinsic model. However, a lack of research remains to identify the data-driven evidence for further research. This paper explores a unified formulation of the loco-manipulation problem using reinforcement learning (RL) by reconfiguring robotic limbs with an overconstrained design into multi-legged and multi-fingered robots. Such design reconfiguration allows for adopting a co-training architecture for reinforcement learning towards a unified loco-manipulation policy. As a result, we find data-driven evidence to support the transferability between locomotion and manipulation skills using a single RL policy with a multilayer perceptron or graph neural network. We also demonstrate the Sim2Real transfer of the learned loco-manipulation skills in a robotic prototype. This work expands the knowledge frontiers on loco-manipulation transferability with learning-based evidence applied in a novel platform with overconstrained robotic limbs.},
keywords = {Authorship - Corresponding, JCR Q1, Jour - Biomimetics (Biomimetics)},
pubstate = {published},
tppubtype = {article}
}
Locomotion and manipulation are two essential skills in robotics but are often divided or decoupled into two separate problems. It is widely accepted that the topological duality between multi-legged locomotion and multi-fingered manipulation shares an intrinsic model. However, a lack of research remains to identify the data-driven evidence for further research. This paper explores a unified formulation of the loco-manipulation problem using reinforcement learning (RL) by reconfiguring robotic limbs with an overconstrained design into multi-legged and multi-fingered robots. Such design reconfiguration allows for adopting a co-training architecture for reinforcement learning towards a unified loco-manipulation policy. As a result, we find data-driven evidence to support the transferability between locomotion and manipulation skills using a single RL policy with a multilayer perceptron or graph neural network. We also demonstrate the Sim2Real transfer of the learned loco-manipulation skills in a robotic prototype. This work expands the knowledge frontiers on loco-manipulation transferability with learning-based evidence applied in a novel platform with overconstrained robotic limbs.
26.
Linhan Yang, Bidan Huang, Qingbiao Li, Ya-Yen Tsai, Wang Wei Lee, Chaoyang Song, Jia Pan
TacGNN: Learning Tactile-based In-hand Manipulation with a Blind Robot using Hierarchical Graph Neural Network Journal Article
In: IEEE Robotics and Automation Letters, vol. 8, iss. June, no. 6, pp. 3605-3612, 2023.
Abstract | Links | BibTeX | Tags: Authorship - Co-Author, JCR Q2, Jour - IEEE Robot. Autom. Lett. (RA-L), Special - Dual-Track
@article{Yang2023TacGNN,
title = {TacGNN: Learning Tactile-based In-hand Manipulation with a Blind Robot using Hierarchical Graph Neural Network},
author = {Linhan Yang and Bidan Huang and Qingbiao Li and Ya-Yen Tsai and Wang Wei Lee and Chaoyang Song and Jia Pan},
doi = {10.1109/LRA.2023.3264759},
year = {2023},
date = {2023-04-05},
urldate = {2023-04-05},
journal = {IEEE Robotics and Automation Letters},
volume = {8},
number = {6},
issue = {June},
pages = {3605-3612},
abstract = {In this letter, we propose a novel framework for tactile-based dexterous manipulation learning with a blind anthropomorphic robotic hand, i.e. without visual sensing. First, object-related states were extracted from the raw tactile signals by a graph-based perception model - TacGNN. The resulting tactile features were then utilized in the policy learning of an in-hand manipulation task in the second stage. This method was examined by a Baoding ball task - simultaneously manipulating two spheres around each other by 180 degrees in hand. We conducted experiments on object states prediction and in-hand manipulation using a reinforcement learning algorithm (PPO). Results show that TacGNN is effective in predicting object-related states during manipulation by decreasing the RMSE of prediction to 0.096 cm comparing to other methods, such as MLP, CNN, and GCN. Finally, the robot hand could finish an in-hand manipulation task solely relying on the robotic own perception - tactile sensing and proprioception. In addition, our methods are tested on three tasks with different difficulty levels and transferred to the real robot without further training.},
keywords = {Authorship - Co-Author, JCR Q2, Jour - IEEE Robot. Autom. Lett. (RA-L), Special - Dual-Track},
pubstate = {published},
tppubtype = {article}
}
In this letter, we propose a novel framework for tactile-based dexterous manipulation learning with a blind anthropomorphic robotic hand, i.e. without visual sensing. First, object-related states were extracted from the raw tactile signals by a graph-based perception model - TacGNN. The resulting tactile features were then utilized in the policy learning of an in-hand manipulation task in the second stage. This method was examined by a Baoding ball task - simultaneously manipulating two spheres around each other by 180 degrees in hand. We conducted experiments on object states prediction and in-hand manipulation using a reinforcement learning algorithm (PPO). Results show that TacGNN is effective in predicting object-related states during manipulation by decreasing the RMSE of prediction to 0.096 cm comparing to other methods, such as MLP, CNN, and GCN. Finally, the robot hand could finish an in-hand manipulation task solely relying on the robotic own perception - tactile sensing and proprioception. In addition, our methods are tested on three tasks with different difficulty levels and transferred to the real robot without further training.
25.
Hao Tian, Chaoyang Song, Changbo Wang, Xinyu Zhang, Jia Pan
Sampling-Based Planning for Retrieving Near-Cylindrical Objects in Cluttered Scenes Using Hierarchical Graphs Journal Article
In: IEEE Transactions on Robotics, vol. 39, iss. February, no. 1, pp. 165-182, 2023.
Abstract | Links | BibTeX | Tags: Authorship - Co-Author, JCR Q1, Jour - IEEE Trans. Robot. (T-RO)
@article{Tian2023SamplingBased,
title = {Sampling-Based Planning for Retrieving Near-Cylindrical Objects in Cluttered Scenes Using Hierarchical Graphs},
author = {Hao Tian and Chaoyang Song and Changbo Wang and Xinyu Zhang and Jia Pan},
doi = {10.1109/TRO.2022.3191596},
year = {2023},
date = {2023-02-01},
urldate = {2023-02-01},
journal = {IEEE Transactions on Robotics},
volume = {39},
number = {1},
issue = {February},
pages = {165-182},
abstract = {We present an incremental sampling-based task and motion planner for retrieving near-cylindrical objects, like bottle, in cluttered scenes, which computes a plan for removing obstacles to generate a collision-free motion of a robot to retrieve the target object. Our proposed planner uses a two-level hierarchy, including the first-level roadmap for the target object motion and the second-level retrieval graph for the entire robot motion, to aid in deciding the order and trajectory of object removal. We use an incremental expansion strategy to update the roadmap and retrieval graph from the collisions between the target object, the robot, and the obstacles, in order to optimize the object removal sequence. The performance of our method is highlighted in several benchmark scenes, including a fixed robotic arm in a cluttered scene with known obstacle locations and a scene, where locations of some objects or even the target object are unknown due to occlusions. Our method can also efficiently solve the high-dimensional planning problem of object retrieval using a mobile manipulator and be combined with the symbolic planner to plan complex multistep tasks. We deploy our method to a physical robot and integrate it with nonprehensile actions to improve operational efficiency. Compared to the state-of-the-art approaches, our method reduces task and motion planning time up to 24.6% with a higher success rate, and still provides a near-optimal plan.},
keywords = {Authorship - Co-Author, JCR Q1, Jour - IEEE Trans. Robot. (T-RO)},
pubstate = {published},
tppubtype = {article}
}
We present an incremental sampling-based task and motion planner for retrieving near-cylindrical objects, like bottle, in cluttered scenes, which computes a plan for removing obstacles to generate a collision-free motion of a robot to retrieve the target object. Our proposed planner uses a two-level hierarchy, including the first-level roadmap for the target object motion and the second-level retrieval graph for the entire robot motion, to aid in deciding the order and trajectory of object removal. We use an incremental expansion strategy to update the roadmap and retrieval graph from the collisions between the target object, the robot, and the obstacles, in order to optimize the object removal sequence. The performance of our method is highlighted in several benchmark scenes, including a fixed robotic arm in a cluttered scene with known obstacle locations and a scene, where locations of some objects or even the target object are unknown due to occlusions. Our method can also efficiently solve the high-dimensional planning problem of object retrieval using a mobile manipulator and be combined with the symbolic planner to plan complex multistep tasks. We deploy our method to a physical robot and integrate it with nonprehensile actions to improve operational efficiency. Compared to the state-of-the-art approaches, our method reduces task and motion planning time up to 24.6% with a higher success rate, and still provides a near-optimal plan.
24.
You Li, Zhuokang Huang, Xiaobo Liu, Yu Jie, Chaoyang Song, Chengzhi Hu
Calibrated Analytical Model for Magnetic Localization of Wireless Capsule Endoscope based on Onboard Sensing Journal Article
In: Robotica, vol. 41, no. 5, pp. 1500-1514, 2023.
Abstract | Links | BibTeX | Tags: Authorship - Co-Author, JCR Q3, Jour - Robotica (ROBO)
@article{Li2023CalibratedAnalytical,
title = {Calibrated Analytical Model for Magnetic Localization of Wireless Capsule Endoscope based on Onboard Sensing},
author = {You Li and Zhuokang Huang and Xiaobo Liu and Yu Jie and Chaoyang Song and Chengzhi Hu},
doi = {10.1017/S0263574722001849},
year = {2023},
date = {2023-01-12},
urldate = {2023-01-12},
journal = {Robotica},
volume = {41},
number = {5},
pages = {1500-1514},
abstract = {We present an incremental sampling-based task and motion planner for retrieving near-cylindrical objects, like bottle, in cluttered scenes, which computes a plan for removing obstacles to generate a collision-free motion of a robot to retrieve the target object. Our proposed planner uses a two-level hierarchy, including the first-level roadmap for the target object motion and the second-level retrieval graph for the entire robot motion, to aid in deciding the order and trajectory of object removal. We use an incremental expansion strategy to update the roadmap and retrieval graph from the collisions between the target object, the robot, and the obstacles, in order to optimize the object removal sequence. The performance of our method is highlighted in several benchmark scenes, including a fixed robotic arm in a cluttered scene with known obstacle locations and a scene, where locations of some objects or even the target object are unknown due to occlusions. Our method can also efficiently solve the high-dimensional planning problem of object retrieval using a mobile manipulator and be combined with the symbolic planner to plan complex multistep tasks. We deploy our method to a physical robot and integrate it with nonprehensile actions to improve operational efficiency. Compared to the state-of-the-art approaches, our method reduces task and motion planning time up to 24.6% with a higher success rate, and still provides a near-optimal plan.},
keywords = {Authorship - Co-Author, JCR Q3, Jour - Robotica (ROBO)},
pubstate = {published},
tppubtype = {article}
}
We present an incremental sampling-based task and motion planner for retrieving near-cylindrical objects, like bottle, in cluttered scenes, which computes a plan for removing obstacles to generate a collision-free motion of a robot to retrieve the target object. Our proposed planner uses a two-level hierarchy, including the first-level roadmap for the target object motion and the second-level retrieval graph for the entire robot motion, to aid in deciding the order and trajectory of object removal. We use an incremental expansion strategy to update the roadmap and retrieval graph from the collisions between the target object, the robot, and the obstacles, in order to optimize the object removal sequence. The performance of our method is highlighted in several benchmark scenes, including a fixed robotic arm in a cluttered scene with known obstacle locations and a scene, where locations of some objects or even the target object are unknown due to occlusions. Our method can also efficiently solve the high-dimensional planning problem of object retrieval using a mobile manipulator and be combined with the symbolic planner to plan complex multistep tasks. We deploy our method to a physical robot and integrate it with nonprehensile actions to improve operational efficiency. Compared to the state-of-the-art approaches, our method reduces task and motion planning time up to 24.6% with a higher success rate, and still provides a near-optimal plan.
23.
Yuping Gu, Shihao Feng, Yuqin Guo, Fang Wan, Jiansheng Dai, Jia Pan, Chaoyang Song
Overconstrained Coaxial Design of Robotic Legs with Omni-directional Locomotion Journal Article
In: Mechanism and Machine Theory, vol. 176, iss. October, pp. 105018, 2022.
Abstract | Links | BibTeX | Tags: Authorship - Corresponding, JCR Q1, Jour - Mech. Mach. Theory (MMT)
@article{Gu2022OverconstrainedCoaxial,
title = {Overconstrained Coaxial Design of Robotic Legs with Omni-directional Locomotion},
author = {Yuping Gu and Shihao Feng and Yuqin Guo and Fang Wan and Jiansheng Dai and Jia Pan and Chaoyang Song},
doi = {10.1016/j.mechmachtheory.2022.105018},
year = {2022},
date = {2022-10-01},
urldate = {2022-10-01},
journal = {Mechanism and Machine Theory},
volume = {176},
issue = {October},
pages = {105018},
abstract = {While being extensively researched in literature, overconstrained linkages’ engineering potential is yet to be explored. This study investigates the design of overconstrained linkages as robotic legs with coaxial actuation starting with the simplest case, Bennett linkage, to establish the theoretical foundations and engineering advantages of a class of overconstrained robots. We proposed a parametric design of the spatial links and joints in alternative forms so that one can fabricate these overconstrained limbs via 3D printing and then attach the linkage coaxially to a pair of servo actuators as a reconfigurable leg module. We adopted multi-objective optimization to refine the design parameters by analyzing its manipulability metric and force transmission, enabling omni-directional ground locomotion projected from a three-dimensional surface workspace. The proposed prototype quadruped was capable of omni-directional locomotion and had a minimal turning radius (0.2 Body Length) using the fewest actuators. We further explored the kinematics and design potentials to generalize the proposed method for all overconstrained 5R and 6R linkages, paving the path for a future direction in overconstrained robotics.},
keywords = {Authorship - Corresponding, JCR Q1, Jour - Mech. Mach. Theory (MMT)},
pubstate = {published},
tppubtype = {article}
}
While being extensively researched in literature, overconstrained linkages’ engineering potential is yet to be explored. This study investigates the design of overconstrained linkages as robotic legs with coaxial actuation starting with the simplest case, Bennett linkage, to establish the theoretical foundations and engineering advantages of a class of overconstrained robots. We proposed a parametric design of the spatial links and joints in alternative forms so that one can fabricate these overconstrained limbs via 3D printing and then attach the linkage coaxially to a pair of servo actuators as a reconfigurable leg module. We adopted multi-objective optimization to refine the design parameters by analyzing its manipulability metric and force transmission, enabling omni-directional ground locomotion projected from a three-dimensional surface workspace. The proposed prototype quadruped was capable of omni-directional locomotion and had a minimal turning radius (0.2 Body Length) using the fewest actuators. We further explored the kinematics and design potentials to generalize the proposed method for all overconstrained 5R and 6R linkages, paving the path for a future direction in overconstrained robotics.
22.
Chaoyang Song, Jianxi Luo, Katja Hölttä-Otto, Warren Seering, Kevin Otto
Crowdfunding for Design Innovation: Prediction Model with Critical Factors Journal Article
In: IEEE Transactions on Engineering Management, vol. 69, iss. August, no. 4, pp. 1565-1576, 2022.
Abstract | Links | BibTeX | Tags: Authorship - Corresponding, Authorship - First Author, JCR Q1, Jour - IEEE Trans. Eng. Manag. (TEM)
@article{Song2022CrowdfunndingFor,
title = {Crowdfunding for Design Innovation: Prediction Model with Critical Factors},
author = {Chaoyang Song and Jianxi Luo and Katja Hölttä-Otto and Warren Seering and Kevin Otto},
doi = {10.1109/tem.2020.3001764},
year = {2022},
date = {2022-08-01},
urldate = {2022-08-01},
journal = {IEEE Transactions on Engineering Management},
volume = {69},
number = {4},
issue = {August},
pages = {1565-1576},
abstract = {Online reward-based crowdfunding campaigns have emerged as an innovative approach for validating demands, discovering early adopters, and seeking learning and feedback in the design processes of innovative products. However, crowdfunding campaigns for innovative products are faced with a high degree of uncertainty and suffer meager rates of success to fulfill their values for design. To guide designers and innovators for crowdfunding campaigns, this article presents a data-driven methodology to build a prediction model with critical factors for crowdfunding success, based on public online crowdfunding campaign data. Specifically, the methodology filters 26 candidate factors in the real-win-worth framework and identifies the critical ones via stepwise regression to predict the amount of crowdfunding. We demonstrate the methods via deriving prediction models and identifying essential factors from three-dimensional printer and smartwatch campaign data on Kickstarter and Indiegogo. The critical factors can guide campaign developments, and the prediction model may evaluate crowdfunding potential of innovations in contexts, to increase the chance of crowdfunding success of innovative products.
},
keywords = {Authorship - Corresponding, Authorship - First Author, JCR Q1, Jour - IEEE Trans. Eng. Manag. (TEM)},
pubstate = {published},
tppubtype = {article}
}
Online reward-based crowdfunding campaigns have emerged as an innovative approach for validating demands, discovering early adopters, and seeking learning and feedback in the design processes of innovative products. However, crowdfunding campaigns for innovative products are faced with a high degree of uncertainty and suffer meager rates of success to fulfill their values for design. To guide designers and innovators for crowdfunding campaigns, this article presents a data-driven methodology to build a prediction model with critical factors for crowdfunding success, based on public online crowdfunding campaign data. Specifically, the methodology filters 26 candidate factors in the real-win-worth framework and identifies the critical ones via stepwise regression to predict the amount of crowdfunding. We demonstrate the methods via deriving prediction models and identifying essential factors from three-dimensional printer and smartwatch campaign data on Kickstarter and Indiegogo. The critical factors can guide campaign developments, and the prediction model may evaluate crowdfunding potential of innovations in contexts, to increase the chance of crowdfunding success of innovative products.
21.
Haokun Wang, Xiaobo Liu, Nuofan Qiu, Ning Guo, Fang Wan, Chaoyang Song
DeepClaw 2.0: A Data Collection Platform for Learning Human Manipulation Journal Article
In: Frontiers in Robotics and AI, vol. 9, pp. 787291, 2022.
Abstract | Links | BibTeX | Tags: Authorship - Corresponding, JCR Q2, Jour - Front. Robot. AI. (FROBT)
@article{Wang2022DeepClaw2.0,
title = {DeepClaw 2.0: A Data Collection Platform for Learning Human Manipulation},
author = {Haokun Wang and Xiaobo Liu and Nuofan Qiu and Ning Guo and Fang Wan and Chaoyang Song},
url = {Sec. Computational Intelligence in Robotics},
doi = {10.3389/frobt.2022.787291},
year = {2022},
date = {2022-03-15},
urldate = {2022-03-15},
journal = {Frontiers in Robotics and AI},
volume = {9},
pages = {787291},
abstract = {Besides direct interaction, human hands are also skilled at using tools to manipulate objects for typical life and work tasks. This paper proposes DeepClaw 2.0 as a low-cost, open-sourced data collection platform for learning human manipulation. We use an RGB-D camera to visually track the motion and deformation of a pair of soft finger networks on a modified kitchen tong operated by human teachers. These fingers can be easily integrated with robotic grippers to bridge the structural mismatch between humans and robots during learning. The deformation of soft finger networks, which reveals tactile information in contact-rich manipulation, is captured passively. We collected a comprehensive sample dataset involving five human demonstrators in ten manipulation tasks with five trials per task. As a low-cost, open-sourced platform, we also developed an intuitive interface that converts the raw sensor data into state-action data for imitation learning problems. For learning-by-demonstration problems, we further demonstrated our dataset’s potential by using real robotic hardware to collect joint actuation data or using a simulated environment when limited access to the hardware.},
keywords = {Authorship - Corresponding, JCR Q2, Jour - Front. Robot. AI. (FROBT)},
pubstate = {published},
tppubtype = {article}
}
Besides direct interaction, human hands are also skilled at using tools to manipulate objects for typical life and work tasks. This paper proposes DeepClaw 2.0 as a low-cost, open-sourced data collection platform for learning human manipulation. We use an RGB-D camera to visually track the motion and deformation of a pair of soft finger networks on a modified kitchen tong operated by human teachers. These fingers can be easily integrated with robotic grippers to bridge the structural mismatch between humans and robots during learning. The deformation of soft finger networks, which reveals tactile information in contact-rich manipulation, is captured passively. We collected a comprehensive sample dataset involving five human demonstrators in ten manipulation tasks with five trials per task. As a low-cost, open-sourced platform, we also developed an intuitive interface that converts the raw sensor data into state-action data for imitation learning problems. For learning-by-demonstration problems, we further demonstrated our dataset’s potential by using real robotic hardware to collect joint actuation data or using a simulated environment when limited access to the hardware.
20.
Youcan Yan, Yajing Shen, Chaoyang Song, Jia Pan
Tactile Super-Resolution Model for Soft Magnetic Skin Journal Article
In: IEEE Robotics and Automation Letters, vol. 7, iss. April, no. 2, pp. 2589-2596, 2022.
Abstract | Links | BibTeX | Tags: Authorship - Co-Author, JCR Q2, Jour - IEEE Robot. Autom. Lett. (RA-L), Special - Dual-Track
@article{Yan2022TactileSuper,
title = {Tactile Super-Resolution Model for Soft Magnetic Skin},
author = {Youcan Yan and Yajing Shen and Chaoyang Song and Jia Pan},
doi = {10.1109/LRA.2022.3141449},
year = {2022},
date = {2022-01-10},
urldate = {2022-01-10},
journal = {IEEE Robotics and Automation Letters},
volume = {7},
number = {2},
issue = {April},
pages = {2589-2596},
abstract = {Tactile sensors of high spatial resolution can provide rich contact information in terms of accurate contact location and force magnitude for robots. However, achieving a high spatial resolution normally requires a high density of tactile sensing cells (or taxels), which will inevitably lead to crowded wire connections, more data acquisition time and probably crosstalk between taxels. An alternative approach to improve the spatial resolution without introducing a high density of taxels is employing super-resolution technology. Here, we propose a novel tactile super-resolution method based on a sinusoidally magnetized soft magnetic skin, by which we have achieved a 15-fold improvement of localization accuracy (from 6 mm to 0.4 mm) as well as the ability to measure the force magnitude. Different from the existing super-resolution methods that rely on overlapping signals of neighbouring taxels, our model only relies on the local information from a single 3-axis taxel and thereby can detect multipoint contact applied on neighboring taxels and work properly even when some of the neighbouring taxels near the contact position are damaged (or unavailable). With this property, our method would be robust to damage and could potentially benefit robotic applications that require multipoint contact detection.},
keywords = {Authorship - Co-Author, JCR Q2, Jour - IEEE Robot. Autom. Lett. (RA-L), Special - Dual-Track},
pubstate = {published},
tppubtype = {article}
}
Tactile sensors of high spatial resolution can provide rich contact information in terms of accurate contact location and force magnitude for robots. However, achieving a high spatial resolution normally requires a high density of tactile sensing cells (or taxels), which will inevitably lead to crowded wire connections, more data acquisition time and probably crosstalk between taxels. An alternative approach to improve the spatial resolution without introducing a high density of taxels is employing super-resolution technology. Here, we propose a novel tactile super-resolution method based on a sinusoidally magnetized soft magnetic skin, by which we have achieved a 15-fold improvement of localization accuracy (from 6 mm to 0.4 mm) as well as the ability to measure the force magnitude. Different from the existing super-resolution methods that rely on overlapping signals of neighbouring taxels, our model only relies on the local information from a single 3-axis taxel and thereby can detect multipoint contact applied on neighboring taxels and work properly even when some of the neighbouring taxels near the contact position are damaged (or unavailable). With this property, our method would be robust to damage and could potentially benefit robotic applications that require multipoint contact detection.
19.
Haiyang Jiang, Xudong Han, Yonglin Jing, Ning Guo, Fang Wan, Chaoyang Song
Rigid-Soft Interactive Design of a Lobster-Inspired Finger Surface for Enhanced Grasping Underwater Journal Article
In: Frontiers in Robotics and AI, vol. 8, pp. 787187, 2021.
Abstract | Links | BibTeX | Tags: Authorship - Corresponding, JCR Q2, Jour - Front. Robot. AI. (FROBT)
@article{Jiang2021RigidSoft,
title = {Rigid-Soft Interactive Design of a Lobster-Inspired Finger Surface for Enhanced Grasping Underwater},
author = {Haiyang Jiang and Xudong Han and Yonglin Jing and Ning Guo and Fang Wan and Chaoyang Song},
url = {Sec. Soft Robotics},
doi = {10.3389/frobt.2021.787187},
year = {2021},
date = {2021-12-22},
urldate = {2021-12-22},
issuetitle = {Section Soft Robotics},
journal = {Frontiers in Robotics and AI},
volume = {8},
pages = {787187},
abstract = {Bio-inspirations from soft-bodied animals provide a rich design source for soft robots, yet limited literature explored the potential enhancement from rigid-bodied ones. This paper draws inspiration from the tooth profiles of the rigid claws of the Boston Lobster, aiming at an enhanced soft finger surface for underwater grasping using an iterative design process. The lobsters distinguish themselves from other marine animals with a pair of claws capable of dexterous object manipulation both on land and underwater. We proposed a 3-stage design iteration process that involves raw imitation, design parametric exploration, and bionic parametric exploitation on the original tooth profiles on the claws of the Boston Lobster. Eventually, 7 finger surface designs were generated and fabricated with soft silicone. We validated each design stage through many vision-based robotic grasping attempts against selected objects from the Evolved Grasping Analysis Dataset (EGAD). Over 14,000 grasp attempts were accumulated on land (71.4%) and underwater (28.6%), where we selected the optimal design through an on-land experiment and further tested its capability underwater. As a result, we observed an 18.2% improvement in grasping success rate at most from a resultant bionic finger surface design, compared with those without the surface, and a 10.4% improvement at most compared with the validation design from the previous literature. Results from this paper are relevant and consistent with the bioresearch earlier in 1911, showing the value of bionics. The results indicate the capability and competence of the optimal bionic finger surface design in an amphibious environment, which can contribute to future research in enhanced underwater grasping using soft robots.},
keywords = {Authorship - Corresponding, JCR Q2, Jour - Front. Robot. AI. (FROBT)},
pubstate = {published},
tppubtype = {article}
}
Bio-inspirations from soft-bodied animals provide a rich design source for soft robots, yet limited literature explored the potential enhancement from rigid-bodied ones. This paper draws inspiration from the tooth profiles of the rigid claws of the Boston Lobster, aiming at an enhanced soft finger surface for underwater grasping using an iterative design process. The lobsters distinguish themselves from other marine animals with a pair of claws capable of dexterous object manipulation both on land and underwater. We proposed a 3-stage design iteration process that involves raw imitation, design parametric exploration, and bionic parametric exploitation on the original tooth profiles on the claws of the Boston Lobster. Eventually, 7 finger surface designs were generated and fabricated with soft silicone. We validated each design stage through many vision-based robotic grasping attempts against selected objects from the Evolved Grasping Analysis Dataset (EGAD). Over 14,000 grasp attempts were accumulated on land (71.4%) and underwater (28.6%), where we selected the optimal design through an on-land experiment and further tested its capability underwater. As a result, we observed an 18.2% improvement in grasping success rate at most from a resultant bionic finger surface design, compared with those without the surface, and a 10.4% improvement at most compared with the validation design from the previous literature. Results from this paper are relevant and consistent with the bioresearch earlier in 1911, showing the value of bionics. The results indicate the capability and competence of the optimal bionic finger surface design in an amphibious environment, which can contribute to future research in enhanced underwater grasping using soft robots.
18.
Sicong Liu, Yuming Zhu, Zicong Zhang, Zhonggui Fang, Jiyong Tan, Jing Peng, Chaoyang Song, Harry Asada, Zheng Wang
Otariidae-Inspired Soft-Robotic Supernumerary Flippers by Fabric Kirigami and Origami Journal Article
In: IEEE/ASME Transactions on Mechatronics, vol. 26, iss. October, no. 5, pp. 2747-2757, 2021.
Abstract | Links | BibTeX | Tags: Authorship - Co-Author, JCR Q1, Jour - IEEE ASME Trans. Mechatron. (TMech)
@article{Liu2021OtariidaeInspired,
title = {Otariidae-Inspired Soft-Robotic Supernumerary Flippers by Fabric Kirigami and Origami},
author = {Sicong Liu and Yuming Zhu and Zicong Zhang and Zhonggui Fang and Jiyong Tan and Jing Peng and Chaoyang Song and Harry Asada and Zheng Wang},
doi = {10.1109/TMECH.2020.3045476},
year = {2021},
date = {2021-10-01},
urldate = {2021-10-01},
journal = {IEEE/ASME Transactions on Mechatronics},
volume = {26},
number = {5},
issue = {October},
pages = {2747-2757},
abstract = {Wearable robotic devices are receiving rapidly growing attentions for human-centered scenarios from medical, rehabilitation, to industrial applications. Supernumerary robotic limbs have been widely investigated for the augmentation of human limb functions, both as fingers and manipulator arms. Soft robotics offers an alternative approach to conventional motor-driven robot limbs toward safer and lighter systems, while pioneering soft supernumerary limbs are strongly limited in payload and dexterity by the soft robotic design approach, as well as the fabrication techniques. In this article, we proposed a wearable supernumerary soft robot for the human forearm, inspired by the fore flippers of otariids (eared seals). A flat flipper design was adopted, differing from the finger- or arm-shaped state-of-the-art works, with multiple soft actuators embedded as different joints for manipulation dexterity. The soft actuators were designed following origami (paper folding) patterns, reinforced by kirigami (paper cutting) fabrics. With this new approach, the proposed soft flipper incorporated eight independent muscles, achieving over 20 times payload to self-weight ratio, while weighing less than 500 g. The versatility, dexterity, and payload capability were experimentally demonstrated using a fabricated prototype with proprietary actuation and control. This article demonstrates the feasibility and unique advantages of origami + kirigami soft robots as a new approach to strong, dexterous, and yet safe and lightweight wearable robotic devices.},
keywords = {Authorship - Co-Author, JCR Q1, Jour - IEEE ASME Trans. Mechatron. (TMech)},
pubstate = {published},
tppubtype = {article}
}
Wearable robotic devices are receiving rapidly growing attentions for human-centered scenarios from medical, rehabilitation, to industrial applications. Supernumerary robotic limbs have been widely investigated for the augmentation of human limb functions, both as fingers and manipulator arms. Soft robotics offers an alternative approach to conventional motor-driven robot limbs toward safer and lighter systems, while pioneering soft supernumerary limbs are strongly limited in payload and dexterity by the soft robotic design approach, as well as the fabrication techniques. In this article, we proposed a wearable supernumerary soft robot for the human forearm, inspired by the fore flippers of otariids (eared seals). A flat flipper design was adopted, differing from the finger- or arm-shaped state-of-the-art works, with multiple soft actuators embedded as different joints for manipulation dexterity. The soft actuators were designed following origami (paper folding) patterns, reinforced by kirigami (paper cutting) fabrics. With this new approach, the proposed soft flipper incorporated eight independent muscles, achieving over 20 times payload to self-weight ratio, while weighing less than 500 g. The versatility, dexterity, and payload capability were experimentally demonstrated using a fabricated prototype with proprietary actuation and control. This article demonstrates the feasibility and unique advantages of origami + kirigami soft robots as a new approach to strong, dexterous, and yet safe and lightweight wearable robotic devices.
17.
Baiyue Wang, Weijie Guo, Shihao Feng, Hongdong Yi, Fang Wan, Chaoyang Song
Volumetrically Enhanced Soft Actuator with Proprioceptive Sensing Journal Article
In: IEEE Robotics and Automation Letters, vol. 6, iss. July, no. 3, pp. 5284-5291, 2021.
Abstract | Links | BibTeX | Tags: Authorship - Corresponding, JCR Q2, Jour - IEEE Robot. Autom. Lett. (RA-L), Special - Dual-Track
@article{Wang2021VolumetricallyEnhanced,
title = {Volumetrically Enhanced Soft Actuator with Proprioceptive Sensing},
author = {Baiyue Wang and Weijie Guo and Shihao Feng and Hongdong Yi and Fang Wan and Chaoyang Song},
doi = {10.1109/LRA.2021.3072859},
year = {2021},
date = {2021-07-01},
urldate = {2021-07-01},
journal = {IEEE Robotics and Automation Letters},
volume = {6},
number = {3},
issue = {July},
pages = {5284-5291},
abstract = {Soft robots often show a superior power-to-weight ratio using highly compliant, light-weight material, which leverages various bio-inspired body designs to generate desirable deformations for life-like motions. In this letter, given that most material used for soft robots is light-weight in general, we propose a volumetrically enhanced design strategy for soft robots, providing a novel design guideline to govern the form factor of soft robots. We present the design, modeling, and optimization of a volumetrically enhanced soft actuator (VESA) with linear and rotary motions, respectively, achieving superior force and torque output, linear and rotary displacement, and overall extension ratio per unit volume. We further explored VESA's proprioceptive sensing capability by validating the output force and torque through analytical modeling and experimental verification. Our results show that the volumetric metrics hold the potential to be used as a practical design guideline to optimize soft robots’ engineering performance.},
keywords = {Authorship - Corresponding, JCR Q2, Jour - IEEE Robot. Autom. Lett. (RA-L), Special - Dual-Track},
pubstate = {published},
tppubtype = {article}
}
Soft robots often show a superior power-to-weight ratio using highly compliant, light-weight material, which leverages various bio-inspired body designs to generate desirable deformations for life-like motions. In this letter, given that most material used for soft robots is light-weight in general, we propose a volumetrically enhanced design strategy for soft robots, providing a novel design guideline to govern the form factor of soft robots. We present the design, modeling, and optimization of a volumetrically enhanced soft actuator (VESA) with linear and rotary motions, respectively, achieving superior force and torque output, linear and rotary displacement, and overall extension ratio per unit volume. We further explored VESA's proprioceptive sensing capability by validating the output force and torque through analytical modeling and experimental verification. Our results show that the volumetric metrics hold the potential to be used as a practical design guideline to optimize soft robots’ engineering performance.
16.
Linhan Yang, Xudong Han, Weijie Guo, Fang Wan, Jia Pan, Chaoyang Song
Learning-based Optoelectronically Innervated Tactile Finger for Rigid-Soft Interactive Grasping Journal Article
In: IEEE Robotics and Automation Letters, vol. 6, iss. April, no. 2, pp. 3817 - 3824, 2021.
Abstract | Links | BibTeX | Tags: Authorship - Corresponding, JCR Q2, Jour - IEEE Robot. Autom. Lett. (RA-L), Special - Dual-Track
@article{Yang2021LearningBased,
title = {Learning-based Optoelectronically Innervated Tactile Finger for Rigid-Soft Interactive Grasping},
author = {Linhan Yang and Xudong Han and Weijie Guo and Fang Wan and Jia Pan and Chaoyang Song},
doi = {10.1109/LRA.2021.3065186},
year = {2021},
date = {2021-04-01},
urldate = {2021-04-01},
booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
journal = {IEEE Robotics and Automation Letters},
volume = {6},
number = {2},
issue = {April},
pages = {3817 - 3824},
address = {Xi’an, China},
abstract = {This letter presents a novel design of a soft tactile finger with omni-directional adaptation using multi-channel optical fibers for rigid-soft interactive grasping. Machine learning methods are used to train a model for real-time prediction of force, torque, and contact using the tactile data collected. We further integrated such fingers in a reconfigurable gripper design with three fingers so that the finger arrangement can be actively adjusted in real-time based on the tactile data collected during grasping, achieving the process of rigid-soft interactive grasping. Detailed sensor calibration and experimental results are also included to further validate the proposed design for enhanced grasping robustness. Video: https://www.youtube.com/watch?v=ynCfSA4FQnY.},
keywords = {Authorship - Corresponding, JCR Q2, Jour - IEEE Robot. Autom. Lett. (RA-L), Special - Dual-Track},
pubstate = {published},
tppubtype = {article}
}
This letter presents a novel design of a soft tactile finger with omni-directional adaptation using multi-channel optical fibers for rigid-soft interactive grasping. Machine learning methods are used to train a model for real-time prediction of force, torque, and contact using the tactile data collected. We further integrated such fingers in a reconfigurable gripper design with three fingers so that the finger arrangement can be actively adjusted in real-time based on the tactile data collected during grasping, achieving the process of rigid-soft interactive grasping. Detailed sensor calibration and experimental results are also included to further validate the proposed design for enhanced grasping robustness. Video: https://www.youtube.com/watch?v=ynCfSA4FQnY.
15.
Youcan Yan, Zhe Hu, Zhengbao Yang, Wenzhen Yuan, Chaoyang Song, Jia Pan, Yajing Shen
Soft Magnetic Skin for Super-Resolution Tactile Sensing with Force Self-Decoupling Journal Article
In: Science Robotics, vol. 6, no. 51, pp. eabc8801, 2021.
Abstract | Links | BibTeX | Tags: Authorship - Co-Author, JCR Q1, Jour - Sci. Robot. (SciRob)
@article{Yan2021SoftMagnetic,
title = {Soft Magnetic Skin for Super-Resolution Tactile Sensing with Force Self-Decoupling},
author = {Youcan Yan and Zhe Hu and Zhengbao Yang and Wenzhen Yuan and Chaoyang Song and Jia Pan and Yajing Shen},
doi = {10.1126/scirobotics.abc8801},
year = {2021},
date = {2021-02-24},
urldate = {2021-02-24},
journal = {Science Robotics},
volume = {6},
number = {51},
pages = {eabc8801},
abstract = {Human skin can sense subtle changes of both normal and shear forces (i.e., self-decoupled) and perceive stimuli with finer resolution than the average spacing between mechanoreceptors (i.e., super-resolved). By contrast, existing tactile sensors for robotic applications are inferior, lacking accurate force decoupling and proper spatial resolution at the same time. Here, we present a soft tactile sensor with self-decoupling and super-resolution abilities by designing a sinusoidally magnetized flexible film (with the thickness ~0.5 millimeters), whose deformation can be detected by a Hall sensor according to the change of magnetic flux densities under external forces. The sensor can accurately measure the normal force and the shear force (demonstrated in one dimension) with a single unit and achieve a 60-fold super-resolved accuracy enhanced by deep learning. By mounting our sensor at the fingertip of a robotic gripper, we show that robots can accomplish challenging tasks such as stably grasping fragile objects under external disturbance and threading a needle via teleoperation. This research provides new insight into tactile sensor design and could be beneficial to various applications in robotics field, such as adaptive grasping, dexterous manipulation, and human-robot interaction.},
keywords = {Authorship - Co-Author, JCR Q1, Jour - Sci. Robot. (SciRob)},
pubstate = {published},
tppubtype = {article}
}
Human skin can sense subtle changes of both normal and shear forces (i.e., self-decoupled) and perceive stimuli with finer resolution than the average spacing between mechanoreceptors (i.e., super-resolved). By contrast, existing tactile sensors for robotic applications are inferior, lacking accurate force decoupling and proper spatial resolution at the same time. Here, we present a soft tactile sensor with self-decoupling and super-resolution abilities by designing a sinusoidally magnetized flexible film (with the thickness ~0.5 millimeters), whose deformation can be detected by a Hall sensor according to the change of magnetic flux densities under external forces. The sensor can accurately measure the normal force and the shear force (demonstrated in one dimension) with a single unit and achieve a 60-fold super-resolved accuracy enhanced by deep learning. By mounting our sensor at the fingertip of a robotic gripper, we show that robots can accomplish challenging tasks such as stably grasping fragile objects under external disturbance and threading a needle via teleoperation. This research provides new insight into tactile sensor design and could be beneficial to various applications in robotics field, such as adaptive grasping, dexterous manipulation, and human-robot interaction.
14.
Fang Wan, Haokun Wang, Jiyuan Wu, Yujia Liu, Sheng Ge, Chaoyang Song
A Reconfigurable Design for Omni-adaptive Grasp Learning Journal Article
In: IEEE Robotics and Automation Letters, vol. 5, iss. July, no. 3, pp. 4210-4217, 2020.
Abstract | Links | BibTeX | Tags: Authorship - Corresponding, JCR Q2, Jour - IEEE Robot. Autom. Lett. (RA-L), Special - Dual-Track
@article{Wan2020AReconfigurable,
title = {A Reconfigurable Design for Omni-adaptive Grasp Learning},
author = {Fang Wan and Haokun Wang and Jiyuan Wu and Yujia Liu and Sheng Ge and Chaoyang Song},
doi = {10.1109/lra.2020.2982059},
year = {2020},
date = {2020-07-01},
urldate = {2020-07-01},
journal = {IEEE Robotics and Automation Letters},
volume = {5},
number = {3},
issue = {July},
pages = {4210-4217},
abstract = {The engineering design of robotic grippers presents an ample design space for optimization towards robust grasping. In this letter, we investigate how learning method can be used to support the design reconfiguration of robotic grippers for grasping using a novel soft structure with omni-directional adaptation. We propose a gripper system that is reconfigurable in terms of the number and arrangement of the proposed finger, which generates a large number of possible design configurations. Such design reconfigurations with omni-adaptive fingers enables us to systematically investigate the optimal arrangement of the fingers towards robust grasping. Furthermore, we adopt a learning-based method as the baseline to benchmark the effectiveness of each design configuration. As a result, we found that the 3-finger radial configuration is suitable for space-saving and cost-effectiveness, achieving an average 96% grasp success rate on seen and novel objects selected from the YCB dataset. The 4-finger radial arrangement can be applied to cases that require a higher payload with even distribution. We achieved dimension reduction using the radial gripper design with the removal of z-axis rotation during grasping. We also reported the different outcomes with or without friction enhancement of the soft finger network.},
keywords = {Authorship - Corresponding, JCR Q2, Jour - IEEE Robot. Autom. Lett. (RA-L), Special - Dual-Track},
pubstate = {published},
tppubtype = {article}
}
The engineering design of robotic grippers presents an ample design space for optimization towards robust grasping. In this letter, we investigate how learning method can be used to support the design reconfiguration of robotic grippers for grasping using a novel soft structure with omni-directional adaptation. We propose a gripper system that is reconfigurable in terms of the number and arrangement of the proposed finger, which generates a large number of possible design configurations. Such design reconfigurations with omni-adaptive fingers enables us to systematically investigate the optimal arrangement of the fingers towards robust grasping. Furthermore, we adopt a learning-based method as the baseline to benchmark the effectiveness of each design configuration. As a result, we found that the 3-finger radial configuration is suitable for space-saving and cost-effectiveness, achieving an average 96% grasp success rate on seen and novel objects selected from the YCB dataset. The 4-finger radial arrangement can be applied to cases that require a higher payload with even distribution. We achieved dimension reduction using the radial gripper design with the removal of z-axis rotation during grasping. We also reported the different outcomes with or without friction enhancement of the soft finger network.
13.
Fang Wan, Chaoyang Song
Flange-Based Hand-Eye Calibration Using a 3D Camera with High Resolution, Accuracy, and Frame Rate Journal Article
In: Frontiers in Robotics and AI, vol. 7, pp. 65, 2020.
Abstract | Links | BibTeX | Tags: Authorship - Corresponding, JCR Q2, Jour - Front. Robot. AI. (FROBT)
@article{Wan2020FlangeBased,
title = {Flange-Based Hand-Eye Calibration Using a 3D Camera with High Resolution, Accuracy, and Frame Rate},
author = {Fang Wan and Chaoyang Song},
doi = {10.3389/frobt.2020.00065},
year = {2020},
date = {2020-05-29},
urldate = {2020-05-29},
journal = {Frontiers in Robotics and AI},
volume = {7},
pages = {65},
abstract = {Point cloud data provides three-dimensional (3D) measurement of the geometric details in the physical world, which relies heavily on the quality of the machine vision system. In this paper, we explore the potentials of a 3D scanner of high quality (15 million points per second), accuracy (up to 0.150 mm), and frame rate (up to 20 FPS) during static and dynamic measurements of the robot flange for direct hand-eye calibration and trajectory error tracking. With the availability of high-quality point cloud data, we can exploit the standardized geometric features on the robot flange for 3D measurement, which are directly accessible for hand-eye calibration problems. In the meanwhile, we tested the proposed flange-based calibration methods in a dynamic setting to capture point cloud data in a high frame rate. We found that our proposed method works robustly even in dynamic environments, enabling a versatile hand-eye calibration during motion. Furthermore, capturing high-quality point cloud data in real-time opens new doors for the use of 3D scanners, capable of detecting sensitive anomalies of refined details even in motion trajectories. Codes and sample data of this calibration method is provided at Github (https://github.com/ancorasir/flange_handeye_calibration).},
keywords = {Authorship - Corresponding, JCR Q2, Jour - Front. Robot. AI. (FROBT)},
pubstate = {published},
tppubtype = {article}
}
Point cloud data provides three-dimensional (3D) measurement of the geometric details in the physical world, which relies heavily on the quality of the machine vision system. In this paper, we explore the potentials of a 3D scanner of high quality (15 million points per second), accuracy (up to 0.150 mm), and frame rate (up to 20 FPS) during static and dynamic measurements of the robot flange for direct hand-eye calibration and trajectory error tracking. With the availability of high-quality point cloud data, we can exploit the standardized geometric features on the robot flange for 3D measurement, which are directly accessible for hand-eye calibration problems. In the meanwhile, we tested the proposed flange-based calibration methods in a dynamic setting to capture point cloud data in a high frame rate. We found that our proposed method works robustly even in dynamic environments, enabling a versatile hand-eye calibration during motion. Furthermore, capturing high-quality point cloud data in real-time opens new doors for the use of 3D scanners, capable of detecting sensitive anomalies of refined details even in motion trajectories. Codes and sample data of this calibration method is provided at Github (https://github.com/ancorasir/flange_handeye_calibration).
12.
Linhan Yang, Fang Wan, Haokun Wang, Xiaobo Liu, Yujia Liu, Jia Pan, Chaoyang Song
Rigid-Soft Interactive Learning for Robust Grasping Journal Article
In: IEEE Robotics and Automation Letters, vol. 5, iss. April, no. 2, pp. 1720 - 1727, 2020.
Abstract | Links | BibTeX | Tags: Authorship - Corresponding, JCR Q2, Jour - IEEE Robot. Autom. Lett. (RA-L), Special - Dual-Track
@article{Yang2020RigidSoft,
title = {Rigid-Soft Interactive Learning for Robust Grasping},
author = {Linhan Yang and Fang Wan and Haokun Wang and Xiaobo Liu and Yujia Liu and Jia Pan and Chaoyang Song},
doi = {10.1109/lra.2020.2969932},
year = {2020},
date = {2020-04-01},
urldate = {2020-04-01},
booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
journal = {IEEE Robotics and Automation Letters},
volume = {5},
number = {2},
issue = {April},
pages = {1720 - 1727},
address = {Paris, France},
abstract = {Robot learning is widely accepted by academia and industry with its potentials to transform autonomous robot control through machine learning. Inspired by widely used soft fingers on grasping, we propose a method of rigid-soft interactive learning, aiming at reducing the time of data collection. In this letter, we classify the interaction categories into Rigid-Rigid, Rigid-Soft, SoftRigid according to the interaction surface between grippers and target objects. We find experimental evidence that the interaction types between grippers and target objects play an essential role in the learning methods. We use soft, stuffed toys for training, instead of everyday objects, to reduce the integration complexity and computational burden. Although the stuffed toys are limited in reflecting the physics of finger-object interaction in real-life scenarios, we exploit such rigid-soft interaction by changing the gripper fingers to the soft ones when dealing with rigid, daily-life items such as the Yale-CMU-Berkeley (YCB) objects. With a small data collection of 5 K picking attempts in total, our results suggest that such Rigid-Soft and Soft-Rigid interactions are transferable. Moreover, the combination of such interactions shows better performance on the grasping test. We also explore the effect of the grasp type on the learning method by changing the gripper configurations. We achieve the best grasping performance at 97.5% for easy YCB objects and 81.3% for difficult YCB objects while using a precise grasp with a two-soft-finger gripper to collect training data and power grasp with a four-soft-finger gripper to test the grasp policy.},
keywords = {Authorship - Corresponding, JCR Q2, Jour - IEEE Robot. Autom. Lett. (RA-L), Special - Dual-Track},
pubstate = {published},
tppubtype = {article}
}
Robot learning is widely accepted by academia and industry with its potentials to transform autonomous robot control through machine learning. Inspired by widely used soft fingers on grasping, we propose a method of rigid-soft interactive learning, aiming at reducing the time of data collection. In this letter, we classify the interaction categories into Rigid-Rigid, Rigid-Soft, SoftRigid according to the interaction surface between grippers and target objects. We find experimental evidence that the interaction types between grippers and target objects play an essential role in the learning methods. We use soft, stuffed toys for training, instead of everyday objects, to reduce the integration complexity and computational burden. Although the stuffed toys are limited in reflecting the physics of finger-object interaction in real-life scenarios, we exploit such rigid-soft interaction by changing the gripper fingers to the soft ones when dealing with rigid, daily-life items such as the Yale-CMU-Berkeley (YCB) objects. With a small data collection of 5 K picking attempts in total, our results suggest that such Rigid-Soft and Soft-Rigid interactions are transferable. Moreover, the combination of such interactions shows better performance on the grasping test. We also explore the effect of the grasp type on the learning method by changing the gripper configurations. We achieve the best grasping performance at 97.5% for easy YCB objects and 81.3% for difficult YCB objects while using a precise grasp with a two-soft-finger gripper to collect training data and power grasp with a four-soft-finger gripper to test the grasp policy.
11.
Juan Yi, Xiaojiao Chen, Chaoyang Song, Jianshu Zhou, Yujia Liu, Sicong Liu, Zheng Wang
Customizable Three-Dimensional-Printed Origami Soft Robotic Joint with Effective Behavior Shaping for Safe Interactions Journal Article
In: IEEE Transactions on Robotics, vol. 35, iss. February, no. 1, pp. 114-123, 2019.
Abstract | Links | BibTeX | Tags: Authorship - Co-Author, JCR Q1, Jour - IEEE Trans. Robot. (T-RO)
@article{Yi2019Customizable3D,
title = {Customizable Three-Dimensional-Printed Origami Soft Robotic Joint with Effective Behavior Shaping for Safe Interactions},
author = {Juan Yi and Xiaojiao Chen and Chaoyang Song and Jianshu Zhou and Yujia Liu and Sicong Liu and Zheng Wang},
doi = {10.1109/tro.2018.2871440},
year = {2019},
date = {2019-02-01},
urldate = {2019-02-01},
journal = {IEEE Transactions on Robotics},
volume = {35},
number = {1},
issue = {February},
pages = {114-123},
abstract = {Fast-growing interests in safe and effective robot–environment interactions stimulated global investigations on soft robotics. The inherent compliance of soft robots ensures promising safety features but drastically reduces force capability, thereby complicating system modeling and control. To tackle these limitations, a soft robotic joint with enhanced strength, servo performance, and impact behavior shaping is proposed in this paper, based on novel three-dimensional-printed soft origami rotary actuators. The complete workflow is presented from the concept of origami design and analytical modeling, joint design, fabrication, control, and validation experiments. The proposed approach facilitates a fully customizable joint design towards the desired force capability and motion range. Validation results from models and experiments using multiple fabricated prototypes proved the excellent performance linearity and superior force capability, with 18.5-N·m maximum torque under 180 kPa, and 300-g self-weight. The behavior shaping capability is achieved by a low-level joint-angle servo and a high-level variable-stiffness regulation; this significantly reduces the impact torque by 53% and ensures powerful and safe interactions. The comprehensive guidelines provide insightful references for soft robotic design for wider robotic applications.},
keywords = {Authorship - Co-Author, JCR Q1, Jour - IEEE Trans. Robot. (T-RO)},
pubstate = {published},
tppubtype = {article}
}
Fast-growing interests in safe and effective robot–environment interactions stimulated global investigations on soft robotics. The inherent compliance of soft robots ensures promising safety features but drastically reduces force capability, thereby complicating system modeling and control. To tackle these limitations, a soft robotic joint with enhanced strength, servo performance, and impact behavior shaping is proposed in this paper, based on novel three-dimensional-printed soft origami rotary actuators. The complete workflow is presented from the concept of origami design and analytical modeling, joint design, fabrication, control, and validation experiments. The proposed approach facilitates a fully customizable joint design towards the desired force capability and motion range. Validation results from models and experiments using multiple fabricated prototypes proved the excellent performance linearity and superior force capability, with 18.5-N·m maximum torque under 180 kPa, and 300-g self-weight. The behavior shaping capability is achieved by a low-level joint-angle servo and a high-level variable-stiffness regulation; this significantly reduces the impact torque by 53% and ensures powerful and safe interactions. The comprehensive guidelines provide insightful references for soft robotic design for wider robotic applications.
10.
Katja Hölttä-Otto, Kevin Otto, Chaoyang Song, Jianxi Luo, Timothy Li, Carolyn C. Seepersad, Warren Seering
The Characteristics of Innovative, Mechanical Products—10 Years Later Journal Article
In: Journal of Mechanical Design, vol. 140, iss. August, no. 8, pp. 084501, 2018.
Abstract | Links | BibTeX | Tags: Authorship - Co-Author, JCR Q2, Jour - J. Mech. Des. (JMD)
@article{HolttaOtto2018TheCharacteristics,
title = {The Characteristics of Innovative, Mechanical Products—10 Years Later},
author = {Katja Hölttä-Otto and Kevin Otto and Chaoyang Song and Jianxi Luo and Timothy Li and Carolyn C. Seepersad and Warren Seering},
doi = {10.1115/1.4039851},
year = {2018},
date = {2018-08-01},
urldate = {2018-08-01},
journal = {Journal of Mechanical Design},
volume = {140},
number = {8},
issue = {August},
pages = {084501},
abstract = {Ten years prior to this paper, innovative mechanical products were analyzed and found to embody multiple innovation characteristics—an average of two more than competing products in the marketplace. At the time, it was not known whether these products would be successful over time and whether the number or type of innovation characteristics would be related with success. In this work, products from the previous study were categorized into well- and under-adopted products. Also, each product was categorized according to the type of firm that launched it: a new venture or an established firm. The innovative products enjoyed a success rate of 77% on average. The success was not dependent on the number or type of innovation characteristics embodied by the product. However, products developed in new ventures embody, on average, one more innovation characteristic and enjoy a slightly higher success rate than those launched by established firms.},
keywords = {Authorship - Co-Author, JCR Q2, Jour - J. Mech. Des. (JMD)},
pubstate = {published},
tppubtype = {article}
}
Ten years prior to this paper, innovative mechanical products were analyzed and found to embody multiple innovation characteristics—an average of two more than competing products in the marketplace. At the time, it was not known whether these products would be successful over time and whether the number or type of innovation characteristics would be related with success. In this work, products from the previous study were categorized into well- and under-adopted products. Also, each product was categorized according to the type of firm that launched it: a new venture or an established firm. The innovative products enjoyed a success rate of 77% on average. The success was not dependent on the number or type of innovation characteristics embodied by the product. However, products developed in new ventures embody, on average, one more innovation characteristic and enjoy a slightly higher success rate than those launched by established firms.
9.
Fang Wan, Chaoyang Song
A Neural Network with Logical Reasoning based on Auxiliary Inputs Journal Article
In: Frontiers in Robotics and AI, vol. 5, pp. 86, 2018.
Abstract | Links | BibTeX | Tags: Authorship - Corresponding, JCR Q2, Jour - Front. Robot. AI. (FROBT)
@article{Wan2018ANeural,
title = {A Neural Network with Logical Reasoning based on Auxiliary Inputs},
author = {Fang Wan and Chaoyang Song},
url = {Sec. Computational Intelligence in Robotics},
doi = {10.3389/frobt.2018.00086},
year = {2018},
date = {2018-07-30},
urldate = {2018-07-30},
journal = {Frontiers in Robotics and AI},
volume = {5},
pages = {86},
abstract = {This paper describes a neural network design using auxiliary inputs, namely the indicators, that act as the hints to explain the predicted outcome through logical reasoning, mimicking the human behavior of deductive reasoning. Besides the original network input and output, we add an auxiliary input that reflects the specific logic of the data to formulate a reasoning process for cross-validation. We found that one can design either meaningful indicators, or even meaningless ones, when using such auxiliary inputs, upon which one can use as the basis of reasoning to explain the predicted outputs. As a result, one can formulate different reasonings to explain the predicted results by designing different sets of auxiliary inputs without the loss of trustworthiness of the outcome. This is similar to human explanation process where one can explain the same observation from different perspectives with reasons. We demonstrate our network concept by using the MNIST data with different sets of auxiliary inputs, where a series of design guidelines are concluded. Later, we validated our results by using a set of images taken from a robotic grasping platform. We found that our network enhanced the last 1–2% of the prediction accuracy while eliminating questionable predictions with self-conflicting logics. Future application of our network with auxiliary inputs can be applied to robotic detection problems such as autonomous object grasping, where the logical reasoning can be introduced to optimize robotic learning.},
keywords = {Authorship - Corresponding, JCR Q2, Jour - Front. Robot. AI. (FROBT)},
pubstate = {published},
tppubtype = {article}
}
This paper describes a neural network design using auxiliary inputs, namely the indicators, that act as the hints to explain the predicted outcome through logical reasoning, mimicking the human behavior of deductive reasoning. Besides the original network input and output, we add an auxiliary input that reflects the specific logic of the data to formulate a reasoning process for cross-validation. We found that one can design either meaningful indicators, or even meaningless ones, when using such auxiliary inputs, upon which one can use as the basis of reasoning to explain the predicted outputs. As a result, one can formulate different reasonings to explain the predicted results by designing different sets of auxiliary inputs without the loss of trustworthiness of the outcome. This is similar to human explanation process where one can explain the same observation from different perspectives with reasons. We demonstrate our network concept by using the MNIST data with different sets of auxiliary inputs, where a series of design guidelines are concluded. Later, we validated our results by using a set of images taken from a robotic grasping platform. We found that our network enhanced the last 1–2% of the prediction accuracy while eliminating questionable predictions with self-conflicting logics. Future application of our network with auxiliary inputs can be applied to robotic detection problems such as autonomous object grasping, where the logical reasoning can be introduced to optimize robotic learning.
8.
Juan Yi, Xiaojiao Chen, Chaoyang Song, Zheng Wang
Fiber-Reinforced Origamic Robotic Actuator Journal Article
In: Soft Robotics, vol. 5, no. 1, pp. 81-92, 2018.
Abstract | Links | BibTeX | Tags: Authorship - Co-Author, JCR Q1, Jour - Soft Robot. (SORO)
@article{Yi2018FiberReinforced,
title = {Fiber-Reinforced Origamic Robotic Actuator},
author = {Juan Yi and Xiaojiao Chen and Chaoyang Song and Zheng Wang},
doi = {10.1089/soro.2016.0079},
year = {2018},
date = {2018-02-01},
urldate = {2018-02-01},
journal = {Soft Robotics},
volume = {5},
number = {1},
pages = {81-92},
abstract = {A novel pneumatic soft linear actuator Fiber-reinforced Origamic Robotic Actuator (FORA) is proposed with significant improvements on the popular McKibben-type actuators, offering nearly doubled motion range, substantially improved force profile, and significantly lower actuation pressure. The desirable feature set is made possible by a novel soft origamic chamber that expands radially while contracts axially when pressurized. Combining this new origamic chamber with a reinforcing fiber mesh, FORA generates very high traction force (over 150N) and very large contractile motion (over 50%) at very low input pressure (100 kPa). We developed quasi-static analytical models both to characterize the motion and forces and as guidelines for actuator design. Fabrication of FORA mostly involves consumer-grade three-dimensional (3D) printing. We provide a detailed list of materials and dimensions. Fabricated FORAs were tested on a dedicated platform against commercially available pneumatic artificial muscles from Shadow and Festo to showcase its superior performances and validate the analytical models with very good agreements. Finally, a robotic joint was developed driven by two antagonistic FORAs, to showcase the benefits of the performance improvements. With its simple structure, fully characterized mechanism, easy fabrication procedure, and highly desirable performance, FORA could be easily customized to application requirements and fabricated by anyone with access to a 3D printer. This will pave the way to the wider adaptation and application of soft robotic systems.},
keywords = {Authorship - Co-Author, JCR Q1, Jour - Soft Robot. (SORO)},
pubstate = {published},
tppubtype = {article}
}
A novel pneumatic soft linear actuator Fiber-reinforced Origamic Robotic Actuator (FORA) is proposed with significant improvements on the popular McKibben-type actuators, offering nearly doubled motion range, substantially improved force profile, and significantly lower actuation pressure. The desirable feature set is made possible by a novel soft origamic chamber that expands radially while contracts axially when pressurized. Combining this new origamic chamber with a reinforcing fiber mesh, FORA generates very high traction force (over 150N) and very large contractile motion (over 50%) at very low input pressure (100 kPa). We developed quasi-static analytical models both to characterize the motion and forces and as guidelines for actuator design. Fabrication of FORA mostly involves consumer-grade three-dimensional (3D) printing. We provide a detailed list of materials and dimensions. Fabricated FORAs were tested on a dedicated platform against commercially available pneumatic artificial muscles from Shadow and Festo to showcase its superior performances and validate the analytical models with very good agreements. Finally, a robotic joint was developed driven by two antagonistic FORAs, to showcase the benefits of the performance improvements. With its simple structure, fully characterized mechanism, easy fabrication procedure, and highly desirable performance, FORA could be easily customized to application requirements and fabricated by anyone with access to a 3D printer. This will pave the way to the wider adaptation and application of soft robotic systems.
7.
Yaohui Chen, Fang Wan, Tong Wu, Chaoyang Song
Soft-Rigid Interaction Mechanism towards a Lobster-inspired Hybrid Actuator Journal Article
In: Journal of Micromechanics and Microengineering, vol. 28, iss. December, no. 1, pp. 014007, 2017.
Abstract | Links | BibTeX | Tags: Authorship - Corresponding, JCR Q2, Jour - J. Micromech. Microeng. (JMM)
@article{Chen2017SoftRigid,
title = {Soft-Rigid Interaction Mechanism towards a Lobster-inspired Hybrid Actuator},
author = {Yaohui Chen and Fang Wan and Tong Wu and Chaoyang Song},
doi = {10.1088/1361-6439/aa9e25},
year = {2017},
date = {2017-12-15},
urldate = {2017-12-15},
issuetitle = {Special Issue on Soft Robotics and Smart System Technologies},
journal = {Journal of Micromechanics and Microengineering},
volume = {28},
number = {1},
issue = {December},
pages = {014007},
abstract = {Soft pneumatic actuators (SPAs) are intrinsically light-weight, compliant and therefore ideal to directly interact with humans and be implemented into wearable robotic devices. However, they also pose new challenges in describing and sensing their continuous deformation. In this paper, we propose a hybrid actuator design with bio-inspirations from the lobsters, which can generate reconfigurable bending movements through the internal soft chamber interacting with the external rigid shells. This design with joint and link structures enables us to exactly track its bending configurations that previously posed a significant challenge to soft robots. Analytic models are developed to illustrate the soft-rigid interaction mechanism with experimental validation. A robotic glove using hybrid actuators to assist grasping is assembled to illustrate their potentials in safe human-robot interactions. Considering all the design merits, our work presents a practical approach to the design of next-generation robots capable of achieving both good accuracy and compliance.},
keywords = {Authorship - Corresponding, JCR Q2, Jour - J. Micromech. Microeng. (JMM)},
pubstate = {published},
tppubtype = {article}
}
Soft pneumatic actuators (SPAs) are intrinsically light-weight, compliant and therefore ideal to directly interact with humans and be implemented into wearable robotic devices. However, they also pose new challenges in describing and sensing their continuous deformation. In this paper, we propose a hybrid actuator design with bio-inspirations from the lobsters, which can generate reconfigurable bending movements through the internal soft chamber interacting with the external rigid shells. This design with joint and link structures enables us to exactly track its bending configurations that previously posed a significant challenge to soft robots. Analytic models are developed to illustrate the soft-rigid interaction mechanism with experimental validation. A robotic glove using hybrid actuators to assist grasping is assembled to illustrate their potentials in safe human-robot interactions. Considering all the design merits, our work presents a practical approach to the design of next-generation robots capable of achieving both good accuracy and compliance.
6.
Chaoyang Song, Huijuan Feng, Yan Chen, I-Ming Chen, Rongjie Kang
Reconfigurable Mechanism Generated from the Network of Bennett Linkages Journal Article
In: Mechanism and Machine Theory, vol. 88, iss. June, pp. 49-62, 2015.
Abstract | Links | BibTeX | Tags: Authorship - First Author, JCR Q1, Jour - Mech. Mach. Theory (MMT)
@article{Song2015ReconfigurableMechanism,
title = {Reconfigurable Mechanism Generated from the Network of Bennett Linkages},
author = {Chaoyang Song and Huijuan Feng and Yan Chen and I-Ming Chen and Rongjie Kang},
doi = {10.1016/j.mechmachtheory.2015.02.003},
year = {2015},
date = {2015-06-01},
urldate = {2015-06-01},
journal = {Mechanism and Machine Theory},
volume = {88},
issue = {June},
pages = {49-62},
abstract = {A network of four Bennett linkages is proposed in this paper. Totally five types of overconstrained 5R and 6R linkages, including the generalized Goldberg 5R linkage, generalized variant of the L-shape Goldberg 6R linkage, Waldron's hybrid 6R linkage, isomerized case of the generalized L-shape Goldberg 6R linkage, and generalized Wohlhart's double-Goldberg 6R linkage, can be constructed by modifying this Bennett network. The 8R linkage formed by Bennett network serves as the basic mechanism to realise the reconfiguration among five types of overconstrained linkages by rigidifying some of the eight joints. The work also reveals the in-depth relationship among the Bennett-based linkages, which provides a substantial advancement in the design of reconfigurable mechanisms using overconstrained linkages.},
keywords = {Authorship - First Author, JCR Q1, Jour - Mech. Mach. Theory (MMT)},
pubstate = {published},
tppubtype = {article}
}
A network of four Bennett linkages is proposed in this paper. Totally five types of overconstrained 5R and 6R linkages, including the generalized Goldberg 5R linkage, generalized variant of the L-shape Goldberg 6R linkage, Waldron's hybrid 6R linkage, isomerized case of the generalized L-shape Goldberg 6R linkage, and generalized Wohlhart's double-Goldberg 6R linkage, can be constructed by modifying this Bennett network. The 8R linkage formed by Bennett network serves as the basic mechanism to realise the reconfiguration among five types of overconstrained linkages by rigidifying some of the eight joints. The work also reveals the in-depth relationship among the Bennett-based linkages, which provides a substantial advancement in the design of reconfigurable mechanisms using overconstrained linkages.
5.
Chaoyang Song, Yan Chen, I-Ming Chen
Kinematic Study of the Original and Revised General Line-Symmetric Bricard 6R Linkages Journal Article
In: Journal of Mechanisms and Robotics, vol. 6, iss. August, no. 3, pp. 031002, 2014.
Abstract | Links | BibTeX | Tags: Authorship - First Author, JCR Q2, Jour - J. Mech. Robot. (JMR)
@article{Song2014KinematicStudy,
title = {Kinematic Study of the Original and Revised General Line-Symmetric Bricard 6R Linkages},
author = {Chaoyang Song and Yan Chen and I-Ming Chen},
doi = {10.1115/1.4026339},
year = {2014},
date = {2014-08-01},
urldate = {2014-08-01},
journal = {Journal of Mechanisms and Robotics},
volume = {6},
number = {3},
issue = {August},
pages = {031002},
abstract = {In this paper, the solutions to closure equations of the original general line-symmetric Bricard 6R linkage are derived through matrix method. Two independent linkage closures are found in the original general line-symmetric Bricard 6R linkage, which are line-symmetric in geometry conditions, kinematic variables and spatial configurations. The revised general line-symmetric Bricard 6R linkage differs from the original linkage with negatively equaled offsets on the opposite joints. Further analysis shows that the revised linkage is equivalent to the original linkage with different setups on joint axis directions. As a special case of the general line-symmetric Bricard linkage, the line-symmetric octahedral Bricard linkage also has two forms in the closure equations. Their closure curves are not independent but joined into a full circle. This work offers an in-depth understanding about the kinematics of the general line-symmetric Bricard linkages.},
keywords = {Authorship - First Author, JCR Q2, Jour - J. Mech. Robot. (JMR)},
pubstate = {published},
tppubtype = {article}
}
In this paper, the solutions to closure equations of the original general line-symmetric Bricard 6R linkage are derived through matrix method. Two independent linkage closures are found in the original general line-symmetric Bricard 6R linkage, which are line-symmetric in geometry conditions, kinematic variables and spatial configurations. The revised general line-symmetric Bricard 6R linkage differs from the original linkage with negatively equaled offsets on the opposite joints. Further analysis shows that the revised linkage is equivalent to the original linkage with different setups on joint axis directions. As a special case of the general line-symmetric Bricard linkage, the line-symmetric octahedral Bricard linkage also has two forms in the closure equations. Their closure curves are not independent but joined into a full circle. This work offers an in-depth understanding about the kinematics of the general line-symmetric Bricard linkages.
4.
Chaoyang Song, Yan Chen, I-Ming Chen
A 6R Linkage Reconfigurable between the Line-Symmetric Bricard Linkage and the Bennett Linkage Journal Article
In: Mechanism and Machine Theory, vol. 70, iss. December, pp. 278-292, 2013.
Abstract | Links | BibTeX | Tags: Authorship - First Author, JCR Q1, Jour - Mech. Mach. Theory (MMT)
@article{Song2013A6R,
title = {A 6R Linkage Reconfigurable between the Line-Symmetric Bricard Linkage and the Bennett Linkage},
author = {Chaoyang Song and Yan Chen and I-Ming Chen},
doi = {10.1016/j.mechmachtheory.2013.07.013},
year = {2013},
date = {2013-12-01},
urldate = {2013-12-01},
journal = {Mechanism and Machine Theory},
volume = {70},
issue = {December},
pages = {278-292},
abstract = {This paper explores the feasibility of constructing mechanisms reconfigurable between 6R and 4R overconstrained linkages. Spatial triangle and Bennett linkage are used as the building blocks to form the reconfigurable Bricard linkage. Due to the different directions of the joint axes, the Bennett linkage can be setup in either asymmetric or line-symmetric manners. Subsequently, two 6R linkages are constructed in asymmetric and line-symmetric configurations, respectively. Their potential of reconfiguration is investigated through bifurcation analysis. The result shows that the asymmetric one can be reconfigured between Bennett linkage and general line-symmetric Bricard linkage through bifurcation points, while the line-symmetric one only functions as a Bennett linkage with two additional fixed joints.},
keywords = {Authorship - First Author, JCR Q1, Jour - Mech. Mach. Theory (MMT)},
pubstate = {published},
tppubtype = {article}
}
This paper explores the feasibility of constructing mechanisms reconfigurable between 6R and 4R overconstrained linkages. Spatial triangle and Bennett linkage are used as the building blocks to form the reconfigurable Bricard linkage. Due to the different directions of the joint axes, the Bennett linkage can be setup in either asymmetric or line-symmetric manners. Subsequently, two 6R linkages are constructed in asymmetric and line-symmetric configurations, respectively. Their potential of reconfiguration is investigated through bifurcation analysis. The result shows that the asymmetric one can be reconfigured between Bennett linkage and general line-symmetric Bricard linkage through bifurcation points, while the line-symmetric one only functions as a Bennett linkage with two additional fixed joints.
3.
Chaoyang Song, Yan Chen
Multiple Linkage Forms and Bifurcation Behaviours of the Double-Subtractive-Goldberg 6R Linkage Journal Article
In: Mechanism and Machine Theory, vol. 57, iss. November, pp. 95-110, 2012.
Abstract | Links | BibTeX | Tags: Authorship - First Author, JCR Q1, Jour - Mech. Mach. Theory (MMT)
@article{Song2012MultipleLinkage,
title = {Multiple Linkage Forms and Bifurcation Behaviours of the Double-Subtractive-Goldberg 6R Linkage},
author = {Chaoyang Song and Yan Chen},
doi = {10.1016/j.mechmachtheory.2012.07.002},
year = {2012},
date = {2012-11-01},
urldate = {2012-11-01},
journal = {Mechanism and Machine Theory},
volume = {57},
issue = {November},
pages = {95-110},
abstract = {In this paper, a particular type of double-subtractive-Goldberg 6R linkage is obtained by combining two subtractive Goldberg 5R linkages on the commonly shared ‘roof-links’ through the common link-pair method and common Bennett-linkage method. Two distinct linkage forms are obtained with the identical geometry conditions, yet different closure equations. Bifurcation behaviours of these two forms are analysed, leading to the discovery of two more linkage forms of this linkage, which cannot be constructed with Bennett linkages or Goldberg linkages directly. From the construction process, this 6R linkage belongs to the Bennett-based linkages. But about the bifurcation behaviours, it is closely related to the line-symmetric Bricard linkage because of its hidden symmetric property. Therefore, it could play an important role in exploring the relationship between the Bennett-based linkages and the Bricard linkages.},
keywords = {Authorship - First Author, JCR Q1, Jour - Mech. Mach. Theory (MMT)},
pubstate = {published},
tppubtype = {article}
}
In this paper, a particular type of double-subtractive-Goldberg 6R linkage is obtained by combining two subtractive Goldberg 5R linkages on the commonly shared ‘roof-links’ through the common link-pair method and common Bennett-linkage method. Two distinct linkage forms are obtained with the identical geometry conditions, yet different closure equations. Bifurcation behaviours of these two forms are analysed, leading to the discovery of two more linkage forms of this linkage, which cannot be constructed with Bennett linkages or Goldberg linkages directly. From the construction process, this 6R linkage belongs to the Bennett-based linkages. But about the bifurcation behaviours, it is closely related to the line-symmetric Bricard linkage because of its hidden symmetric property. Therefore, it could play an important role in exploring the relationship between the Bennett-based linkages and the Bricard linkages.
2.
Chaoyang Song, Yan Chen
A Family of Mixed Double-Goldberg 6R Linkages Journal Article
In: Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 468, no. 2139, pp. 871-890, 2012.
Abstract | Links | BibTeX | Tags: Authorship - First Author, JCR Q1, Jour - Proc. Math. Phys. Eng. Sci. (RoyalSocA)
@article{Song2012AFamily,
title = {A Family of Mixed Double-Goldberg 6R Linkages},
author = {Chaoyang Song and Yan Chen},
doi = {10.1098/rspa.2011.0345},
year = {2012},
date = {2012-03-08},
urldate = {2012-03-08},
journal = {Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences},
volume = {468},
number = {2139},
pages = {871-890},
abstract = {A complete family of double-Goldberg 6R linkages is reported in this article by combining a subtractive Goldberg 5R linkage and a Goldberg 5R linkage through the common link-pair or common Bennett-linkage method. A number of distinct types of overconstrained linkages are built, namely the mixed double-Goldberg 6R linkages. They all have one degree of freedom and their closure equations are derived in detail. One of them degenerates into a Goldberg 5R linkage. From the construction process and geometry conditions, the corresponding relationship between the newly found 6R linkages and the double-Goldberg 6R linkages, constructed from two Goldberg 5R linkages or two subtractive Goldberg 5R linkages, has been established.},
keywords = {Authorship - First Author, JCR Q1, Jour - Proc. Math. Phys. Eng. Sci. (RoyalSocA)},
pubstate = {published},
tppubtype = {article}
}
A complete family of double-Goldberg 6R linkages is reported in this article by combining a subtractive Goldberg 5R linkage and a Goldberg 5R linkage through the common link-pair or common Bennett-linkage method. A number of distinct types of overconstrained linkages are built, namely the mixed double-Goldberg 6R linkages. They all have one degree of freedom and their closure equations are derived in detail. One of them degenerates into a Goldberg 5R linkage. From the construction process and geometry conditions, the corresponding relationship between the newly found 6R linkages and the double-Goldberg 6R linkages, constructed from two Goldberg 5R linkages or two subtractive Goldberg 5R linkages, has been established.
1.
Chaoyang Song, Yan Chen
A Spatial 6R Linkage Derived from Subtractive Goldberg 5R Linkages Journal Article
In: Mechanism and Machine Theory, vol. 46, iss. August, no. 8, pp. 1097-1106, 2011.
Abstract | Links | BibTeX | Tags: Authorship - First Author, JCR Q1, Jour - Mech. Mach. Theory (MMT)
@article{Song2011ASpatial,
title = {A Spatial 6R Linkage Derived from Subtractive Goldberg 5R Linkages},
author = {Chaoyang Song and Yan Chen},
doi = {10.1016/j.mechmachtheory.2011.03.006},
year = {2011},
date = {2011-08-01},
urldate = {2011-08-01},
journal = {Mechanism and Machine Theory},
volume = {46},
number = {8},
issue = {August},
pages = {1097-1106},
abstract = {In this paper, a subtractive Goldberg 5R linkage is defined as a variation of Goldberg 5R linkage. A spatial 6R linkage is constructed by combining two subtractive Goldberg 5R linkages through a common Bennett linkage. This 6R linkage, namely double subtractive Goldberg 6R linkage, appears to be distinct from other existing spatial 6R overconstrained linkages reported before. Both the overconstrained geometric conditions and the closure equations of the proposed linkage are derived. Physical models are also made to validate the linkage.},
keywords = {Authorship - First Author, JCR Q1, Jour - Mech. Mach. Theory (MMT)},
pubstate = {published},
tppubtype = {article}
}
In this paper, a subtractive Goldberg 5R linkage is defined as a variation of Goldberg 5R linkage. A spatial 6R linkage is constructed by combining two subtractive Goldberg 5R linkages through a common Bennett linkage. This 6R linkage, namely double subtractive Goldberg 6R linkage, appears to be distinct from other existing spatial 6R overconstrained linkages reported before. Both the overconstrained geometric conditions and the closure equations of the proposed linkage are derived. Physical models are also made to validate the linkage.
Conference Papers
35.
Haoran Sun, Shihao Feng, Bangchao Huang, Zishang Zhang, Ronghan Xu, Guojing Huang, Guangyi Huang, Jiayi Yin, Nuofan Qiu, Hua Chen, Wei Zhang, Jia Pan, Fang Wan, Chaoyang Song
Overconstrained Locomotion Conference
International Symposium of Robotics Research (ISRR2024), Long Beach, California, USA, 2024, (Accepted).
Abstract | Links | BibTeX | Tags: Authorship - Corresponding, Conf - ISRR
@conference{Sun2024OCLocomotion,
title = {Overconstrained Locomotion},
author = {Haoran Sun and Shihao Feng and Bangchao Huang and Zishang Zhang and Ronghan Xu and Guojing Huang and Guangyi Huang and Jiayi Yin and Nuofan Qiu and Hua Chen and Wei Zhang and Jia Pan and Fang Wan and Chaoyang Song},
url = {https://isrr2024.su.domains/},
doi = {10.48550/arXiv.2310.09824},
year = {2024},
date = {2024-12-08},
urldate = {2024-12-08},
booktitle = {International Symposium of Robotics Research (ISRR2024)},
address = {Long Beach, California, USA},
abstract = {This paper studies the design, modeling, and control of a novel robotic limb that produces overconstrained locomotion by employing the Bennett linkage for motion generation, capable of parametric reconfiguration between a reptile- and mammal-inspired morphology within a single quadruped. In contrast to the prevailing focus on planar linkages, this research delves into adopting overconstrained linkages as the limb mechanism. The overconstrained linkages have solid theoretical foundations in advanced kinematics but are under-explored in robotic applications. This study showcases the morphological superiority of Overconstrained Robotic Limbs (ORLs) that can transform into planar or spherical limbs, exemplified using the simplest case of a Bennett linkage as an ORL. We apply Model Predictive Control (MPC) to simulate a range of overconstrained locomotion tasks, revealing its superiority in energy efficiency against planar limbs when considering foothold distances and speeds. From an evolutionary biology perspective, these findings highlight the mechanism distinctions in limb design between reptiles and mammals and represent the first documented instance of ORLs outperforming planar limb designs in dynamic locomotion.},
note = {Accepted},
keywords = {Authorship - Corresponding, Conf - ISRR},
pubstate = {published},
tppubtype = {conference}
}
This paper studies the design, modeling, and control of a novel robotic limb that produces overconstrained locomotion by employing the Bennett linkage for motion generation, capable of parametric reconfiguration between a reptile- and mammal-inspired morphology within a single quadruped. In contrast to the prevailing focus on planar linkages, this research delves into adopting overconstrained linkages as the limb mechanism. The overconstrained linkages have solid theoretical foundations in advanced kinematics but are under-explored in robotic applications. This study showcases the morphological superiority of Overconstrained Robotic Limbs (ORLs) that can transform into planar or spherical limbs, exemplified using the simplest case of a Bennett linkage as an ORL. We apply Model Predictive Control (MPC) to simulate a range of overconstrained locomotion tasks, revealing its superiority in energy efficiency against planar limbs when considering foothold distances and speeds. From an evolutionary biology perspective, these findings highlight the mechanism distinctions in limb design between reptiles and mammals and represent the first documented instance of ORLs outperforming planar limb designs in dynamic locomotion.
34.
Linhan Yang, Lei Yang, Haoran Sun, Zeqing Zhang, Haibin He, Fang Wan, Chaoyang Song, Jia Pan
One Fling to Goal: Environment-aware Dynamics for Goal-conditioned Fabric Flinging Conference
Workshop on the Algorithmic Foundations of Robotics (WAFR2024), Chicago, USA, 2024, (Accepted).
Abstract | Links | BibTeX | Tags: Authorship - Corresponding, Conf - WAFR
@conference{Yang2024OneFling,
title = {One Fling to Goal: Environment-aware Dynamics for Goal-conditioned Fabric Flinging},
author = {Linhan Yang and Lei Yang and Haoran Sun and Zeqing Zhang and Haibin He and Fang Wan and Chaoyang Song and Jia Pan},
url = {https://www.algorithmic-robotics.org/},
doi = {10.48550/arXiv.2406.14136},
year = {2024},
date = {2024-10-07},
urldate = {2024-10-07},
booktitle = {Workshop on the Algorithmic Foundations of Robotics (WAFR2024)},
address = {Chicago, USA},
abstract = {Fabric manipulation dynamically is commonly seen in manufacturing and domestic settings. While dynamically manipulating a fabric piece to reach a target state is highly efficient, this task presents considerable challenges due to the varying properties of different fabrics, complex dynamics when interacting with environments, and meeting required goal conditions. To address these challenges, we present One Fling to Goal, an algorithm capable of handling fabric pieces with diverse shapes and physical properties across various scenarios. Our method learns a graph-based dynamics model equipped with environmental awareness. With this dynamics model, we devise a real-time controller to enable high-speed fabric manipulation in one attempt, requiring less than 3 seconds to finish the goal-conditioned task. We experimentally validate our method on a goal-conditioned manipulation task in five diverse scenarios. Our method significantly improves this goal-conditioned task, achieving an average error of 13.2mm in complex scenarios. Our method can be seamlessly transferred to real-world robotic systems and generalized to unseen scenarios in a zero-shot manner.},
note = {Accepted},
keywords = {Authorship - Corresponding, Conf - WAFR},
pubstate = {published},
tppubtype = {conference}
}
Fabric manipulation dynamically is commonly seen in manufacturing and domestic settings. While dynamically manipulating a fabric piece to reach a target state is highly efficient, this task presents considerable challenges due to the varying properties of different fabrics, complex dynamics when interacting with environments, and meeting required goal conditions. To address these challenges, we present One Fling to Goal, an algorithm capable of handling fabric pieces with diverse shapes and physical properties across various scenarios. Our method learns a graph-based dynamics model equipped with environmental awareness. With this dynamics model, we devise a real-time controller to enable high-speed fabric manipulation in one attempt, requiring less than 3 seconds to finish the goal-conditioned task. We experimentally validate our method on a goal-conditioned manipulation task in five diverse scenarios. Our method significantly improves this goal-conditioned task, achieving an average error of 13.2mm in complex scenarios. Our method can be seamlessly transferred to real-world robotic systems and generalized to unseen scenarios in a zero-shot manner.
33.
Yenan Chen, Chuye Zhang, Pengxi Gu, Jianuo Qiu, Jiayi Yin, Nuofan Qiu, Guojing Huang, Bangchao Huang, Zishang Zhang, Hui Deng, Wei Zhang, Fang Wan, Chaoyang Song
IEEE/IFToMM International Conference on Reconfigurable Mechanisms and Robots (ReMAR2024), Chicago, USA, 2024.
Abstract | Links | BibTeX | Tags: Authorship - Corresponding, Conf - ReMAR
@conference{Chen2024EvolutionaryMorphology,
title = {Evolutionary Morphology Towards Overconstrained Locomotion via Large-Scale, Multi-Terrain Deep Reinforcement Learning},
author = {Yenan Chen and Chuye Zhang and Pengxi Gu and Jianuo Qiu and Jiayi Yin and Nuofan Qiu and Guojing Huang and Bangchao Huang and Zishang Zhang and Hui Deng and Wei Zhang and Fang Wan and Chaoyang Song},
url = {https://iftomm-world.org/conferences/remar2024/#:~:text=Following%20successful%20completion%20in%20London%20(2009),%20Tianjin%20(2012),%20Beijing},
doi = {10.1109/ReMAR61031.2024.10618090},
year = {2024},
date = {2024-06-23},
urldate = {2024-06-23},
booktitle = {IEEE/IFToMM International Conference on Reconfigurable Mechanisms and Robots (ReMAR2024)},
address = {Chicago, USA},
abstract = {While the animals' Fin-to-Limb evolution has been well-researched in biology, such morphological trans- formation remains under-adopted in the modern design of advanced robotic limbs. This paper investigates a novel class of overconstrained locomotion from a design and learning perspective inspired by evolutionary morphology, aiming to integrate the concept of 'intelligent design under constraints' - hereafter referred to as constraint-driven design intelligence - in developing modern robotic limbs with superior energy efficiency. We propose a 3D-printable design of robotic limbs parametrically reconfigurable as a classical planar 4-bar linkage, an overconstrained Bennett linkage, and a spherical 4-bar linkage. These limbs adopt a co-axial actuation, identical to the modern legged robot platforms, with the added capability of upgrading into a wheel-legged system. Then, we implemented a large-scale, multi-terrain deep reinforcement learning framework to train these reconfigurable limbs for a comparative analysis of overconstrained locomotion in energy efficiency. Results show that the overconstrained limbs exhibit more efficient locomotion than planar limbs during forward and sideways walking over different terrains, including floors, slopes, and stairs, with or without random noises, by saving at least 22% mechanical energy in completing the traverse task, with the spherical limbs being the least efficient. It also achieves the highest average speed of 0.85m/s on flat terrain, which is 20% faster than the planar limbs. This study paves the path for an exciting direction for future research in overconstrained robotics leveraging evolutionary morphology and reconfigurable mechanism intelligence when combined with state-of-the-art methods in deep reinforcement learning.},
keywords = {Authorship - Corresponding, Conf - ReMAR},
pubstate = {published},
tppubtype = {conference}
}
While the animals' Fin-to-Limb evolution has been well-researched in biology, such morphological trans- formation remains under-adopted in the modern design of advanced robotic limbs. This paper investigates a novel class of overconstrained locomotion from a design and learning perspective inspired by evolutionary morphology, aiming to integrate the concept of 'intelligent design under constraints' - hereafter referred to as constraint-driven design intelligence - in developing modern robotic limbs with superior energy efficiency. We propose a 3D-printable design of robotic limbs parametrically reconfigurable as a classical planar 4-bar linkage, an overconstrained Bennett linkage, and a spherical 4-bar linkage. These limbs adopt a co-axial actuation, identical to the modern legged robot platforms, with the added capability of upgrading into a wheel-legged system. Then, we implemented a large-scale, multi-terrain deep reinforcement learning framework to train these reconfigurable limbs for a comparative analysis of overconstrained locomotion in energy efficiency. Results show that the overconstrained limbs exhibit more efficient locomotion than planar limbs during forward and sideways walking over different terrains, including floors, slopes, and stairs, with or without random noises, by saving at least 22% mechanical energy in completing the traverse task, with the spherical limbs being the least efficient. It also achieves the highest average speed of 0.85m/s on flat terrain, which is 20% faster than the planar limbs. This study paves the path for an exciting direction for future research in overconstrained robotics leveraging evolutionary morphology and reconfigurable mechanism intelligence when combined with state-of-the-art methods in deep reinforcement learning.
32.
Sen Li, Fang Wan, Chaoyang Song
Active Surface with Passive Omni-Directional Adaptation for In-Hand Manipulation Conference
IEEE/IFToMM International Conference on Reconfigurable Mechanisms and Robots (ReMAR2024), Chicago, USA, 2024.
Abstract | Links | BibTeX | Tags: Authorship - Corresponding, Conf - ReMAR
@conference{Li2024ActiveSurface,
title = {Active Surface with Passive Omni-Directional Adaptation for In-Hand Manipulation},
author = {Sen Li and Fang Wan and Chaoyang Song},
url = {https://iftomm-world.org/conferences/remar2024/#:~:text=Following%20successful%20completion%20in%20London%20(2009),%20Tianjin%20(2012),%20Beijing},
doi = {10.1109/ReMAR61031.2024.10619925},
year = {2024},
date = {2024-06-23},
urldate = {2024-06-23},
booktitle = {IEEE/IFToMM International Conference on Reconfigurable Mechanisms and Robots (ReMAR2024)},
address = {Chicago, USA},
abstract = {Soft fingers with omni-directional adaptability ex- cel in 3D twisting, outperforming two-dimensional self-adaptive hands using a finger rotation mechanism to achieve similar adaptability. In this study, we present the design of a soft robotic finger with an active surface on an omni-adaptive structure, which can be easily installed on existing grippers and achieve stability and dexterity for in-hand manipulation. The system’s active surfaces initially transfer the object from the fingertip segment with less compliance to the middle segment of the finger with superior adaptability. Despite the omni-directional deformation of the finger, in-hand manipulation can still be executed with controlled active surfaces. We characterized the soft finger’s stiffness distribution and simplified models to assess the feasibility of lifting and reorienting a grasped object in a 3D twisting state. A set of experiments on in-hand manipulation was performed with the proposed fingers, demonstrating the dexterity and robustness of the strategy.},
keywords = {Authorship - Corresponding, Conf - ReMAR},
pubstate = {published},
tppubtype = {conference}
}
Soft fingers with omni-directional adaptability ex- cel in 3D twisting, outperforming two-dimensional self-adaptive hands using a finger rotation mechanism to achieve similar adaptability. In this study, we present the design of a soft robotic finger with an active surface on an omni-adaptive structure, which can be easily installed on existing grippers and achieve stability and dexterity for in-hand manipulation. The system’s active surfaces initially transfer the object from the fingertip segment with less compliance to the middle segment of the finger with superior adaptability. Despite the omni-directional deformation of the finger, in-hand manipulation can still be executed with controlled active surfaces. We characterized the soft finger’s stiffness distribution and simplified models to assess the feasibility of lifting and reorienting a grasped object in a 3D twisting state. A set of experiments on in-hand manipulation was performed with the proposed fingers, demonstrating the dexterity and robustness of the strategy.
31.
Nuofan Qiu, Fang Wan, Chaoyang Song
Describing Robots from Design to Learning: Towards an Interactive Lifecycle Representation of Robots Conference Forthcoming
IEEE International Conference on Advanced Robotics and Mechatronics (ICARM2024), Tokyo, Japan, Forthcoming, (Accepted).
Abstract | Links | BibTeX | Tags: Authorship - Corresponding, Conf - ICARM
@conference{Qiu2024DescribingRobots,
title = {Describing Robots from Design to Learning: Towards an Interactive Lifecycle Representation of Robots},
author = {Nuofan Qiu and Fang Wan and Chaoyang Song},
url = {http://www.ieee-arm.org/},
doi = {10.48550/arXiv.2312.12295},
year = {2024},
date = {2024-06-08},
urldate = {2024-06-08},
booktitle = {IEEE International Conference on Advanced Robotics and Mechatronics (ICARM2024)},
address = {Tokyo, Japan},
abstract = {The robot development process is divided into several stages, which create barriers to the exchange of information between these different stages. We advocate for an interactive lifecycle representation, extending from robot morphology design to learning, and introduce the role of robot description formats in facilitating information transfer throughout this pipeline. We analyzed the relationship between design and simulation, enabling us to employ robot process automation methods for transferring information from the design phase to the learning phase in simulation. As part of this effort, we have developed an open-source plugin called ACDC4Robot for Fusion 360, which automates this process and transforms Fusion 360 into a user-friendly graphical interface for creating and editing robot description formats. Additionally, we offer an out-of-the-box robot model library to streamline and reduce repetitive tasks. All codes are hosted open-source. (https://github.com/bionicdl-sustech/ACDC4Robot)},
note = {Accepted},
keywords = {Authorship - Corresponding, Conf - ICARM},
pubstate = {forthcoming},
tppubtype = {conference}
}
The robot development process is divided into several stages, which create barriers to the exchange of information between these different stages. We advocate for an interactive lifecycle representation, extending from robot morphology design to learning, and introduce the role of robot description formats in facilitating information transfer throughout this pipeline. We analyzed the relationship between design and simulation, enabling us to employ robot process automation methods for transferring information from the design phase to the learning phase in simulation. As part of this effort, we have developed an open-source plugin called ACDC4Robot for Fusion 360, which automates this process and transforms Fusion 360 into a user-friendly graphical interface for creating and editing robot description formats. Additionally, we offer an out-of-the-box robot model library to streamline and reduce repetitive tasks. All codes are hosted open-source. (https://github.com/bionicdl-sustech/ACDC4Robot)
30.
Tianyu Wu, Yujian Dong, Yang Xiao, Jinqi Wei, Fang Wan, Chaoyang Song
Vision-based, Low-cost, Soft Robotic Tongs for Shareable and Reproducible Tactile Learning Conference Forthcoming
IEEE International Conference on Advanced Robotics and Mechatronics (ICARM2024), Tokyo, Japan, Forthcoming, (Accepted).
Abstract | Links | BibTeX | Tags: Authorship - Corresponding, Conf - ICARM
@conference{Wu2024VisionBasedb,
title = {Vision-based, Low-cost, Soft Robotic Tongs for Shareable and Reproducible Tactile Learning},
author = {Tianyu Wu and Yujian Dong and Yang Xiao and Jinqi Wei and Fang Wan and Chaoyang Song},
url = {http://www.ieee-arm.org/},
year = {2024},
date = {2024-06-01},
urldate = {2024-06-01},
booktitle = {IEEE International Conference on Advanced Robotics and Mechatronics (ICARM2024)},
address = {Tokyo, Japan},
abstract = {Recent research shows a growing interest in adopting touch interaction for robot learning, yet it remains challenging to efficiently acquire high-quality, structured tactile data at a low cost. In this study, we propose the design of vision-based soft robotic tongs to generate reproducible and shareable data of tactile interaction for learning. We further developed a web-based platform for convenient data collection and a portable assembly that can be deployed within minutes. We trained a simple network to infer the 6D force and torque using relative pose data from markers on the fingers and reached a reasonably high accuracy (an MAE of 0.548 N at 60 Hz within [0,20] N) but cost only 50 USD per set. The recorded tactile data is downloadable for robot learning. We further demonstrated the system for interacting with robotic arms in manipulation learning and remote control. We have open-sourced the system on GitHub with further information. (https://github.com/bionicdl-sustech/SoftRoboticTongs)},
note = {Accepted},
keywords = {Authorship - Corresponding, Conf - ICARM},
pubstate = {forthcoming},
tppubtype = {conference}
}
Recent research shows a growing interest in adopting touch interaction for robot learning, yet it remains challenging to efficiently acquire high-quality, structured tactile data at a low cost. In this study, we propose the design of vision-based soft robotic tongs to generate reproducible and shareable data of tactile interaction for learning. We further developed a web-based platform for convenient data collection and a portable assembly that can be deployed within minutes. We trained a simple network to infer the 6D force and torque using relative pose data from markers on the fingers and reached a reasonably high accuracy (an MAE of 0.548 N at 60 Hz within [0,20] N) but cost only 50 USD per set. The recorded tactile data is downloadable for robot learning. We further demonstrated the system for interacting with robotic arms in manipulation learning and remote control. We have open-sourced the system on GitHub with further information. (https://github.com/bionicdl-sustech/SoftRoboticTongs)
29.
Xudong Han, Sheng Liu, Fang Wan, Chaoyang Song
Vision-based Tactile Sensing for an Omni-adaptive Soft Finger Conference
IEEE International Conference on Development and Learning (ICDL2023), Macau SAR, 2023.
Abstract | Links | BibTeX | Tags: Authorship - Corresponding, Conf - ICDL
@conference{Han2023VisionBased,
title = {Vision-based Tactile Sensing for an Omni-adaptive Soft Finger},
author = {Xudong Han and Sheng Liu and Fang Wan and Chaoyang Song},
url = {https://www.proceedings.com/content/072/072332webtoc.pdf},
doi = {10.1109/ICDL55364.2023.10364455},
year = {2023},
date = {2023-11-09},
urldate = {2023-11-09},
booktitle = {IEEE International Conference on Development and Learning (ICDL2023)},
address = {Macau SAR},
abstract = {Vision-based tactile sensing provides a novel solution to robotic proprioception using visual information to infer physical interaction on the contact surface. In this paper, we leveraged the omni-adaptive capability of a soft finger with differential stiffness by adding a monocular camera at its bottom to track its spatial deformation while interacting with objects. We modeled this soft finger's physical interaction and measured the stiffness distribution through experiments. The camera captured the soft finger's deformation when interacting with probes for different contact forces and positions. Using a neural network modified from AlexNet, we proposed a preliminary estimation model of the contact force and position using the captured images. The results show that the proposed method can achieve an accuracy of 90% for position estimation and a normalized root mean squared error of 3.4% for force estimation, showing the reliability and robustness of the proposed sensing method.},
keywords = {Authorship - Corresponding, Conf - ICDL},
pubstate = {published},
tppubtype = {conference}
}
Vision-based tactile sensing provides a novel solution to robotic proprioception using visual information to infer physical interaction on the contact surface. In this paper, we leveraged the omni-adaptive capability of a soft finger with differential stiffness by adding a monocular camera at its bottom to track its spatial deformation while interacting with objects. We modeled this soft finger's physical interaction and measured the stiffness distribution through experiments. The camera captured the soft finger's deformation when interacting with probes for different contact forces and positions. Using a neural network modified from AlexNet, we proposed a preliminary estimation model of the contact force and position using the captured images. The results show that the proposed method can achieve an accuracy of 90% for position estimation and a normalized root mean squared error of 3.4% for force estimation, showing the reliability and robustness of the proposed sensing method.
28.
Linhan Yang, Bidan Huang, Qingbiao Li, Ya-Yen Tsai, Wang Wei Lee, Chaoyang Song, Jia Pan
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS2023), Huntington Place, Detroit, Michigan, USA, 2023, (Dual-track Submission with RAL: https://doi.org/10.1109/LRA.2023.3264759).
Abstract | Links | BibTeX | Tags: Authorship - Co-Author, Conf - IROS, Special - Dual-Track
@conference{Yang2023TacGNN-IROS,
title = {TacGNN: Learning Tactile-Based In-Hand Manipulation with a Blind Robot Using Hierarchical Graph Neural Network},
author = {Linhan Yang and Bidan Huang and Qingbiao Li and Ya-Yen Tsai and Wang Wei Lee and Chaoyang Song and Jia Pan},
url = {https://ieee-iros.org/},
year = {2023},
date = {2023-10-01},
urldate = {2023-10-01},
booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS2023)},
address = {Huntington Place, Detroit, Michigan, USA},
abstract = {In this letter, we propose a novel framework for tactile-based dexterous manipulation learning with a blind anthropomorphic robotic hand, i.e. without visual sensing. First, object-related states were extracted from the raw tactile signals by a graph-based perception model - TacGNN. The resulting tactile features were then utilized in the policy learning of an in-hand manipulation task in the second stage. This method was examined by a Baoding ball task - simultaneously manipulating two spheres around each other by 180 degrees in hand. We conducted experiments on object states prediction and in-hand manipulation using a reinforcement learning algorithm (PPO). Results show that TacGNN is effective in predicting object-related states during manipulation by decreasing the RMSE of prediction to 0.096 cm comparing to other methods, such as MLP, CNN, and GCN. Finally, the robot hand could finish an in-hand manipulation task solely relying on the robotic own perception - tactile sensing and proprioception. In addition, our methods are tested on three tasks with different difficulty levels and transferred to the real robot without further training.},
note = {Dual-track Submission with RAL: https://doi.org/10.1109/LRA.2023.3264759},
keywords = {Authorship - Co-Author, Conf - IROS, Special - Dual-Track},
pubstate = {published},
tppubtype = {conference}
}
In this letter, we propose a novel framework for tactile-based dexterous manipulation learning with a blind anthropomorphic robotic hand, i.e. without visual sensing. First, object-related states were extracted from the raw tactile signals by a graph-based perception model - TacGNN. The resulting tactile features were then utilized in the policy learning of an in-hand manipulation task in the second stage. This method was examined by a Baoding ball task - simultaneously manipulating two spheres around each other by 180 degrees in hand. We conducted experiments on object states prediction and in-hand manipulation using a reinforcement learning algorithm (PPO). Results show that TacGNN is effective in predicting object-related states during manipulation by decreasing the RMSE of prediction to 0.096 cm comparing to other methods, such as MLP, CNN, and GCN. Finally, the robot hand could finish an in-hand manipulation task solely relying on the robotic own perception - tactile sensing and proprioception. In addition, our methods are tested on three tasks with different difficulty levels and transferred to the real robot without further training.
27.
Xiaobo Liu, Fang Wan, Sheng Ge, Haokun Wang, Haoran Sun, Chaoyang Song
Jigsaw-based Benchmarking for Learning Robotic Manipulation Honorable Mention Conference
IEEE International Conference on Advanced Robotics and Mechatronics (ICARM2023), Sanya, China, 2023.
Abstract | Links | BibTeX | Tags: Authorship - Corresponding, Award - Best Conference Paper Finalist, Award - Paper, Conf - ICARM
@conference{Liu2023JigsawBased,
title = {Jigsaw-based Benchmarking for Learning Robotic Manipulation},
author = {Xiaobo Liu and Fang Wan and Sheng Ge and Haokun Wang and Haoran Sun and Chaoyang Song},
url = {http://www.ieee-arm.org/icarm2023/},
doi = {10.1109/ICARM58088.2023.10218784},
year = {2023},
date = {2023-07-08},
urldate = {2023-07-08},
booktitle = {IEEE International Conference on Advanced Robotics and Mechatronics (ICARM2023)},
address = {Sanya, China},
abstract = {Benchmarking provides experimental evidence of the scientific baseline to enhance the progression of fundamental research, which is also applicable to robotics. In this paper, we propose a method to benchmark metrics of robotic manipulation, which addresses the spatial-temporal reasoning skills for robot learning with the jigsaw game. In particular, our approach exploits a simple set of jigsaw pieces by designing a structured protocol, which can be highly customizable according to a wide range of task specifications. Researchers can selectively adopt the proposed protocol to benchmark their research outputs, on a comparable scale in the functional, task, and system-level of details. The purpose is to provide a potential look-up table for learning-based robot manipulation, commonly available in other engineering disciplines, to facilitate the adoption of robotics through calculated, empirical, and systematic experimental evidence.},
keywords = {Authorship - Corresponding, Award - Best Conference Paper Finalist, Award - Paper, Conf - ICARM},
pubstate = {published},
tppubtype = {conference}
}
Benchmarking provides experimental evidence of the scientific baseline to enhance the progression of fundamental research, which is also applicable to robotics. In this paper, we propose a method to benchmark metrics of robotic manipulation, which addresses the spatial-temporal reasoning skills for robot learning with the jigsaw game. In particular, our approach exploits a simple set of jigsaw pieces by designing a structured protocol, which can be highly customizable according to a wide range of task specifications. Researchers can selectively adopt the proposed protocol to benchmark their research outputs, on a comparable scale in the functional, task, and system-level of details. The purpose is to provide a potential look-up table for learning-based robot manipulation, commonly available in other engineering disciplines, to facilitate the adoption of robotics through calculated, empirical, and systematic experimental evidence.
26.
Yuqin Guo, Rongzheng Zhang, Wanghongjie Qiu, Harry Asada, Fang Wan, Chaoyang Song
Underwater Intention Recognition using Head Motion and Throat Vibration for Supernumerary Robotic Assistance Best Paper Conference
IEEE International Conference on Automation Science and Engineering (CASE2023), Auckland, New Zealand, 2023.
Abstract | Links | BibTeX | Tags: Authorship - Corresponding, Award - Best Healthcare Automation Paper, Award - Paper, Conf - CASE
@conference{Guo2023UnderwaterIntention,
title = {Underwater Intention Recognition using Head Motion and Throat Vibration for Supernumerary Robotic Assistance},
author = {Yuqin Guo and Rongzheng Zhang and Wanghongjie Qiu and Harry Asada and Fang Wan and Chaoyang Song},
url = {https://case2023.org/},
doi = {10.1109/CASE56687.2023.10260480},
year = {2023},
date = {2023-06-26},
urldate = {2023-06-26},
booktitle = {IEEE International Conference on Automation Science and Engineering (CASE2023)},
address = {Auckland, New Zealand},
abstract = {This study presents a multi-modal mechanism for recognizing human intentions while diving underwater, aiming to achieve natural human-robot interactions through an underwater superlimb for diving assistance. The underwater environment severely limits the divers' capabilities in intention expression, which becomes more challenging when they intend to operate tools while keeping control of body postures in 3D with the various diving suits and gears. The current literature is limited in underwater intention recognition, impeding the development of intelligent wearable systems for human-robot interactions underwater. Here, we present a novel solution to simultaneously detect head motion and throat vibrations under the water in a compact, wearable design. Experiment results show that using machine learning algorithms, we achieved high performance in integrating these two modalities to translate human intentions to robot control commands for an underwater superlimb system. This study's results paved the way for future development in underwater intention recognition and underwater human-robot interactions with supernumerary support.},
keywords = {Authorship - Corresponding, Award - Best Healthcare Automation Paper, Award - Paper, Conf - CASE},
pubstate = {published},
tppubtype = {conference}
}
This study presents a multi-modal mechanism for recognizing human intentions while diving underwater, aiming to achieve natural human-robot interactions through an underwater superlimb for diving assistance. The underwater environment severely limits the divers' capabilities in intention expression, which becomes more challenging when they intend to operate tools while keeping control of body postures in 3D with the various diving suits and gears. The current literature is limited in underwater intention recognition, impeding the development of intelligent wearable systems for human-robot interactions underwater. Here, we present a novel solution to simultaneously detect head motion and throat vibrations under the water in a compact, wearable design. Experiment results show that using machine learning algorithms, we achieved high performance in integrating these two modalities to translate human intentions to robot control commands for an underwater superlimb system. This study's results paved the way for future development in underwater intention recognition and underwater human-robot interactions with supernumerary support.
25.
Juan Yi, Xiaojiao Chen, Zhonggui Fang, Yujia Liu, Dehao Duanmu, Yinyin Su, Chaoyang Song, Sicong Liu, Zheng Wang
A Soft Wearable Elbow Skeleton for Safe Motion Assistance by Variable Stiffness Conference
ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (DETC/CIE2022), Cleveland, Ohio, USA, 2022.
Abstract | Links | BibTeX | Tags: Authorship - Co-Author, Conf - DETC/CIE
@conference{Yi2022ASoft,
title = {A Soft Wearable Elbow Skeleton for Safe Motion Assistance by Variable Stiffness},
author = {Juan Yi and Xiaojiao Chen and Zhonggui Fang and Yujia Liu and Dehao Duanmu and Yinyin Su and Chaoyang Song and Sicong Liu and Zheng Wang},
doi = {10.1115/DETC2022-90320},
year = {2022},
date = {2022-08-14},
urldate = {2022-08-14},
booktitle = {ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (DETC/CIE2022)},
address = {Cleveland, Ohio, USA},
abstract = {Wearable robots could provide external physical assist, contributing to the well-being of elderly or disabled users in accomplishing tasks or therapeutic procedures. However, closely integrating robot dynamics into human activity demands for safety, pleasance and effectiveness simultaneously, requiring both strength and compliance. Soft robotics is generally regarded as a suitable alternative to rigid motor-based actuators for their light weight and passive compliance. However, existing approaches either have predetermined and/or limited passive compliance, or have moderate payload or motion range. While proven very successful in hand actuation in terms of various robotic gloves, their fundamental limitations restrict further expansion to driving other human body parts with higher demands in payload and speed. Previously we have developed an origami soft robotic joint with high torque, customizable dimensions and motion range. In this paper, we report the most recent results on controller development and wearable system integration of the proposed soft actuator in achieving excellent variable-stiffness compliant performance with inherent safety. Using the newly proposed controller, we demonstrate that a higher stiffness leads to quicker passive recovery than human’s normal reaction, suitable in stabilizing common object; while a more submissive configuration, represented by a lower stiffness, could delay the submissive interaction time allowing for a more delicate control of the dynamics of the interaction port with further involvement of human commands, suitable for objects requiring smoother dynamics like preventing a cup of coffee from sloshing. The proposed control strategy and framework has been implemented in a 3D-scanned, 3D-printed wearable robot with elbow actuation, to demonstrate the advantages and new features through experimental results.},
keywords = {Authorship - Co-Author, Conf - DETC/CIE},
pubstate = {published},
tppubtype = {conference}
}
Wearable robots could provide external physical assist, contributing to the well-being of elderly or disabled users in accomplishing tasks or therapeutic procedures. However, closely integrating robot dynamics into human activity demands for safety, pleasance and effectiveness simultaneously, requiring both strength and compliance. Soft robotics is generally regarded as a suitable alternative to rigid motor-based actuators for their light weight and passive compliance. However, existing approaches either have predetermined and/or limited passive compliance, or have moderate payload or motion range. While proven very successful in hand actuation in terms of various robotic gloves, their fundamental limitations restrict further expansion to driving other human body parts with higher demands in payload and speed. Previously we have developed an origami soft robotic joint with high torque, customizable dimensions and motion range. In this paper, we report the most recent results on controller development and wearable system integration of the proposed soft actuator in achieving excellent variable-stiffness compliant performance with inherent safety. Using the newly proposed controller, we demonstrate that a higher stiffness leads to quicker passive recovery than human’s normal reaction, suitable in stabilizing common object; while a more submissive configuration, represented by a lower stiffness, could delay the submissive interaction time allowing for a more delicate control of the dynamics of the interaction port with further involvement of human commands, suitable for objects requiring smoother dynamics like preventing a cup of coffee from sloshing. The proposed control strategy and framework has been implemented in a 3D-scanned, 3D-printed wearable robot with elbow actuation, to demonstrate the advantages and new features through experimental results.
24.
Youcan Yan, Yajing Shen, Chaoyang Song, Jia Pan
Tactile Super-Resolution Model for Soft Magnetic Skin Conference
IEEE International Conference on Robotics and Automation (ICRA2022), Philadelphia (PA), USA, 2022, (Dual-track Submission with RAL: https://doi.org/10.1109/LRA.2022.3141449).
Abstract | Links | BibTeX | Tags: Authorship - Co-Author, Conf - ICRA, Special - Dual-Track
@conference{Yan2022TactileSuper-ICRA,
title = {Tactile Super-Resolution Model for Soft Magnetic Skin},
author = {Youcan Yan and Yajing Shen and Chaoyang Song and Jia Pan},
doi = {10.1109/LRA.2022.3141449},
year = {2022},
date = {2022-01-10},
urldate = {2022-01-10},
booktitle = {IEEE International Conference on Robotics and Automation (ICRA2022)},
issue = {2},
address = {Philadelphia (PA), USA},
abstract = {Tactile sensors of high spatial resolution can provide rich contact information in terms of accurate contact location and force magnitude for robots. However, achieving a high spatial resolution normally requires a high density of tactile sensing cells (or taxels), which will inevitably lead to crowded wire connections, more data acquisition time and probably crosstalk between taxels. An alternative approach to improve the spatial resolution without introducing a high density of taxels is employing super-resolution technology. Here, we propose a novel tactile super-resolution method based on a sinusoidally magnetized soft magnetic skin, by which we have achieved a 15-fold improvement of localization accuracy (from 6 mm to 0.4 mm) as well as the ability to measure the force magnitude. Different from the existing super-resolution methods that rely on overlapping signals of neighbouring taxels, our model only relies on the local information from a single 3-axis taxel and thereby can detect multipoint contact applied on neighboring taxels and work properly even when some of the neighbouring taxels near the contact position are damaged (or unavailable). With this property, our method would be robust to damage and could potentially benefit robotic applications that require multipoint contact detection.},
note = {Dual-track Submission with RAL: https://doi.org/10.1109/LRA.2022.3141449},
keywords = {Authorship - Co-Author, Conf - ICRA, Special - Dual-Track},
pubstate = {published},
tppubtype = {conference}
}
Tactile sensors of high spatial resolution can provide rich contact information in terms of accurate contact location and force magnitude for robots. However, achieving a high spatial resolution normally requires a high density of tactile sensing cells (or taxels), which will inevitably lead to crowded wire connections, more data acquisition time and probably crosstalk between taxels. An alternative approach to improve the spatial resolution without introducing a high density of taxels is employing super-resolution technology. Here, we propose a novel tactile super-resolution method based on a sinusoidally magnetized soft magnetic skin, by which we have achieved a 15-fold improvement of localization accuracy (from 6 mm to 0.4 mm) as well as the ability to measure the force magnitude. Different from the existing super-resolution methods that rely on overlapping signals of neighbouring taxels, our model only relies on the local information from a single 3-axis taxel and thereby can detect multipoint contact applied on neighboring taxels and work properly even when some of the neighbouring taxels near the contact position are damaged (or unavailable). With this property, our method would be robust to damage and could potentially benefit robotic applications that require multipoint contact detection.
23.
Fang Wan, Xiaobo Liu, Ning Guo, Xudong Han, Feng Tian, Chaoyang Song
Visual Learning Towards Soft Robot Force Control using a 3D Metamaterial with Differential Stiffness Conference
Conference on Robot Learning (CoRL2021), London & Virtual, 2021.
Abstract | Links | BibTeX | Tags: Authorship - Corresponding, Conf - CoRL
@conference{Wan2022VisualLearning,
title = {Visual Learning Towards Soft Robot Force Control using a 3D Metamaterial with Differential Stiffness},
author = {Fang Wan and Xiaobo Liu and Ning Guo and Xudong Han and Feng Tian and Chaoyang Song},
url = {https://proceedings.mlr.press/v164/wan22a/wan22a.pdf},
year = {2021},
date = {2021-11-08},
urldate = {2021-11-08},
booktitle = {Conference on Robot Learning (CoRL2021)},
address = {London & Virtual},
abstract = {This paper explores the feasibility of learning robot force control and interaction using soft metamaterial and machine vision. We start by investigating the differential stiffness of a hollow, cone-shaped, 3D metamaterial made from soft rubber, achieving a large stiffness ratio between the axial and radial directions that leads to an adaptive form response in omni-directions during physical interaction. Then, using image data collected from its internal deformation during various interactions, we explored two similar designs but different learning strategies to estimate force control and interactions on the end-effector of a UR10 e-series robot arm. One is to directly learn the force and torque response from raw images of the metamaterial’s internal deformation. The other is to indirectly estimate the 6D force and torque using a neural network by visually tracking the 6D pose of a marker fixed inside the 3D metamaterial. Finally, we integrated the two proposed systems and achieved similar force feedback and control interactions in simple tasks such as circle following and text writing. Our results show that the learning method holds the potential to support the concept of soft robot force control, providing an intuitive interface at a low cost for robotic systems, generating comparable and capable performances against classical force and torque sensors.},
keywords = {Authorship - Corresponding, Conf - CoRL},
pubstate = {published},
tppubtype = {conference}
}
This paper explores the feasibility of learning robot force control and interaction using soft metamaterial and machine vision. We start by investigating the differential stiffness of a hollow, cone-shaped, 3D metamaterial made from soft rubber, achieving a large stiffness ratio between the axial and radial directions that leads to an adaptive form response in omni-directions during physical interaction. Then, using image data collected from its internal deformation during various interactions, we explored two similar designs but different learning strategies to estimate force control and interactions on the end-effector of a UR10 e-series robot arm. One is to directly learn the force and torque response from raw images of the metamaterial’s internal deformation. The other is to indirectly estimate the 6D force and torque using a neural network by visually tracking the 6D pose of a marker fixed inside the 3D metamaterial. Finally, we integrated the two proposed systems and achieved similar force feedback and control interactions in simple tasks such as circle following and text writing. Our results show that the learning method holds the potential to support the concept of soft robot force control, providing an intuitive interface at a low cost for robotic systems, generating comparable and capable performances against classical force and torque sensors.
22.
Shihao Feng, Yuping Gu, Weijie Guo, Yuqin Guo, Fang Wan, Jia Pan, Chaoyang Song
An Overconstrained Robotic Leg with Coaxial Quasi-direct Drives for Omni-directional Ground Mobility Conference
IEEE International Conference on Robotics and Automation (ICRA2021), Xi’an, China, 2021.
Abstract | Links | BibTeX | Tags: Authorship - Corresponding, Conf - ICRA
@conference{Feng2021AnOverconstrained,
title = {An Overconstrained Robotic Leg with Coaxial Quasi-direct Drives for Omni-directional Ground Mobility},
author = {Shihao Feng and Yuping Gu and Weijie Guo and Yuqin Guo and Fang Wan and Jia Pan and Chaoyang Song},
doi = {10.1109/ICRA48506.2021.9561829},
year = {2021},
date = {2021-05-30},
urldate = {2021-05-30},
booktitle = {IEEE International Conference on Robotics and Automation (ICRA2021)},
address = {Xi’an, China},
abstract = {Planar mechanisms dominate modern designs of legged robots with remote actuator placement for robust agility in ground mobility. This paper presents a novel design of robotic leg modules using the Bennett linkage, driven by two coaxially arranged quasi-direct actuators capable of omnidirectional ground locomotion. The Bennett linkage belongs to a family of overconstrained linkages with three-dimensional spatial motion and unparalleled joint axes. We present the first work regarding the design, modeling, and optimization of the Bennett leg module, enabling lateral locomotion, like the crabs, that was not capable with robotic legs designed with common planar mechanisms. We further explored the concept of overconstrained robots, which is a class of advanced robots based on the design reconfiguration of the Bennett leg modules, serving as a potential direction for future research.},
keywords = {Authorship - Corresponding, Conf - ICRA},
pubstate = {published},
tppubtype = {conference}
}
Planar mechanisms dominate modern designs of legged robots with remote actuator placement for robust agility in ground mobility. This paper presents a novel design of robotic leg modules using the Bennett linkage, driven by two coaxially arranged quasi-direct actuators capable of omnidirectional ground locomotion. The Bennett linkage belongs to a family of overconstrained linkages with three-dimensional spatial motion and unparalleled joint axes. We present the first work regarding the design, modeling, and optimization of the Bennett leg module, enabling lateral locomotion, like the crabs, that was not capable with robotic legs designed with common planar mechanisms. We further explored the concept of overconstrained robots, which is a class of advanced robots based on the design reconfiguration of the Bennett leg modules, serving as a potential direction for future research.
21.
Linhan Yang, Xudong Han, Weijie Guo, Fang Wan, Jia Pan, Chaoyang Song
Learning-based Optoelectronically Innervated Tactile Finger for Rigid-Soft Interactive Grasping Conference
IEEE International Conference on Robotics and Automation (ICRA2021), Xi’an, China, 2021, (Dual-track Submission with RAL: https://doi.org/10.1109/LRA.2021.3065186).
Abstract | Links | BibTeX | Tags: Authorship - Corresponding, Conf - ICRA, Special - Dual-Track
@conference{Yang2021LearningBased-ICRA,
title = {Learning-based Optoelectronically Innervated Tactile Finger for Rigid-Soft Interactive Grasping},
author = {Linhan Yang and Xudong Han and Weijie Guo and Fang Wan and Jia Pan and Chaoyang Song},
doi = {10.1109/LRA.2021.3065186},
year = {2021},
date = {2021-05-30},
urldate = {2021-05-30},
booktitle = {IEEE International Conference on Robotics and Automation (ICRA2021)},
issue = {2},
address = {Xi’an, China},
abstract = {This letter presents a novel design of a soft tactile finger with omni-directional adaptation using multi-channel optical fibers for rigid-soft interactive grasping. Machine learning methods are used to train a model for real-time prediction of force, torque, and contact using the tactile data collected. We further integrated such fingers in a reconfigurable gripper design with three fingers so that the finger arrangement can be actively adjusted in real-time based on the tactile data collected during grasping, achieving the process of rigid-soft interactive grasping. Detailed sensor calibration and experimental results are also included to further validate the proposed design for enhanced grasping robustness.},
note = {Dual-track Submission with RAL: https://doi.org/10.1109/LRA.2021.3065186},
keywords = {Authorship - Corresponding, Conf - ICRA, Special - Dual-Track},
pubstate = {published},
tppubtype = {conference}
}
This letter presents a novel design of a soft tactile finger with omni-directional adaptation using multi-channel optical fibers for rigid-soft interactive grasping. Machine learning methods are used to train a model for real-time prediction of force, torque, and contact using the tactile data collected. We further integrated such fingers in a reconfigurable gripper design with three fingers so that the finger arrangement can be actively adjusted in real-time based on the tactile data collected during grasping, achieving the process of rigid-soft interactive grasping. Detailed sensor calibration and experimental results are also included to further validate the proposed design for enhanced grasping robustness.
20.
Weijie Guo, Baiyue Wang, Shihao Feng, Hongdong Yi, Fang Wan, Chaoyang Song
Volumetrically Enhanced Soft Actuator with Proprioceptive Sensing Conference
IEEE International Conference on Soft Robotics (RoboSoft2021), New Haven, CT, USA, 2021, (Dual-track Submission with RAL: https://doi.org/10.1109/LRA.2021.3072859).
Abstract | Links | BibTeX | Tags: Authorship - Corresponding, Conf - RoboSoft, Special - Dual-Track
@conference{Guo2021VolumetricallyEnhanced,
title = {Volumetrically Enhanced Soft Actuator with Proprioceptive Sensing},
author = {Weijie Guo and Baiyue Wang and Shihao Feng and Hongdong Yi and Fang Wan and Chaoyang Song},
doi = {10.1109/LRA.2021.3072859},
year = {2021},
date = {2021-04-12},
urldate = {2021-04-12},
booktitle = {IEEE International Conference on Soft Robotics (RoboSoft2021)},
address = {New Haven, CT, USA},
abstract = {Soft robots often show a superior power-to-weight ratio using highly compliant, light-weight material, which leverages various bio-inspired body designs to generate desirable deformations for life-like motions. In this letter, given that most material used for soft robots is light-weight in general, we propose a volumetrically enhanced design strategy for soft robots, providing a novel design guideline to govern the form factor of soft robots. We present the design, modeling, and optimization of a volumetrically enhanced soft actuator (VESA) with linear and rotary motions, respectively, achieving superior force and torque output, linear and rotary displacement, and overall extension ratio per unit volume. We further explored VESA's proprioceptive sensing capability by validating the output force and torque through analytical modeling and experimental verification. Our results show that the volumetric metrics hold the potential to be used as a practical design guideline to optimize soft robots’ engineering performance.},
note = {Dual-track Submission with RAL: https://doi.org/10.1109/LRA.2021.3072859},
keywords = {Authorship - Corresponding, Conf - RoboSoft, Special - Dual-Track},
pubstate = {published},
tppubtype = {conference}
}
Soft robots often show a superior power-to-weight ratio using highly compliant, light-weight material, which leverages various bio-inspired body designs to generate desirable deformations for life-like motions. In this letter, given that most material used for soft robots is light-weight in general, we propose a volumetrically enhanced design strategy for soft robots, providing a novel design guideline to govern the form factor of soft robots. We present the design, modeling, and optimization of a volumetrically enhanced soft actuator (VESA) with linear and rotary motions, respectively, achieving superior force and torque output, linear and rotary displacement, and overall extension ratio per unit volume. We further explored VESA's proprioceptive sensing capability by validating the output force and torque through analytical modeling and experimental verification. Our results show that the volumetric metrics hold the potential to be used as a practical design guideline to optimize soft robots’ engineering performance.
19.
Haiyang Jiang, Yonglin Jing, Ning Guo, Weijie Guo, Fang Wan, Chaoyang Song
Lobster-inspired Finger Surface Design for Grasping with Enhanced Robustness Conference
IEEE International Conference on Soft Robotics (RoboSoft2021), New Haven, CT, USA, 2021.
Links | BibTeX | Tags: Authorship - Corresponding, Conf - RoboSoft
@conference{Jiang2021LobsterInspired,
title = {Lobster-inspired Finger Surface Design for Grasping with Enhanced Robustness},
author = {Haiyang Jiang and Yonglin Jing and Ning Guo and Weijie Guo and Fang Wan and Chaoyang Song},
doi = {10.1109/RoboSoft51838.2021.9479215},
year = {2021},
date = {2021-04-12},
urldate = {2021-04-12},
booktitle = {IEEE International Conference on Soft Robotics (RoboSoft2021)},
address = {New Haven, CT, USA},
keywords = {Authorship - Corresponding, Conf - RoboSoft},
pubstate = {published},
tppubtype = {conference}
}
18.
Fang Wan, Haokun Wang, Xiaobo Liu, Linhan Yang, Chaoyang Song
DeepClaw: A Robotic Hardware Benchmarking Platform for Learning Object Manipulation Conference
IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM2020), Boston, MA, USA, 2020.
Links | BibTeX | Tags: Authorship - Corresponding, Conf - AIM
@conference{Wan2020DeepClaw1.0,
title = {DeepClaw: A Robotic Hardware Benchmarking Platform for Learning Object Manipulation},
author = {Fang Wan and Haokun Wang and Xiaobo Liu and Linhan Yang and Chaoyang Song},
doi = {10.1109/aim43001.2020.9159011},
year = {2020},
date = {2020-07-06},
urldate = {2020-07-06},
booktitle = {IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM2020)},
address = {Boston, MA, USA},
keywords = {Authorship - Corresponding, Conf - AIM},
pubstate = {published},
tppubtype = {conference}
}
17.
Linhan Yang, Fang Wan, Haokun Wang, Xiaobo Liu, Yujia Liu, Jia Pan, Chaoyang Song
Rigid-Soft Interactive Learning for Robust Grasping Conference
IEEE International Conference on Robotics and Automation (ICRA2020), Paris, France, 2020, (Dual-track Submission with RAL: https://doi.org/10.1109/lra.2020.2969932).
Abstract | Links | BibTeX | Tags: Authorship - Corresponding, Conf - ICRA, Special - Dual-Track
@conference{Yang2020RigidSoft-ICRA,
title = {Rigid-Soft Interactive Learning for Robust Grasping},
author = {Linhan Yang and Fang Wan and Haokun Wang and Xiaobo Liu and Yujia Liu and Jia Pan and Chaoyang Song},
doi = {10.1109/LRA.2020.2969932},
year = {2020},
date = {2020-05-31},
urldate = {2020-05-31},
booktitle = {IEEE International Conference on Robotics and Automation (ICRA2020)},
address = {Paris, France},
abstract = {Robot learning is widely accepted by academia and industry with its potentials to transform autonomous robot control through machine learning. Inspired by widely used soft fingers on grasping, we propose a method of rigid-soft interactive learning, aiming at reducing the time of data collection. In this letter, we classify the interaction categories into Rigid-Rigid, Rigid-Soft, SoftRigid according to the interaction surface between grippers and target objects. We find experimental evidence that the interaction types between grippers and target objects play an essential role in the learning methods. We use soft, stuffed toys for training, instead of everyday objects, to reduce the integration complexity and computational burden. Although the stuffed toys are limited in reflecting the physics of finger-object interaction in real-life scenarios, we exploit such rigid-soft interaction by changing the gripper fingers to the soft ones when dealing with rigid, daily-life items such as the Yale-CMU-Berkeley (YCB) objects. With a small data collection of 5 K picking attempts in total, our results suggest that such Rigid-Soft and Soft-Rigid interactions are transferable. Moreover, the combination of such interactions shows better performance on the grasping test. We also explore the effect of the grasp type on the learning method by changing the gripper configurations. We achieve the best grasping performance at 97.5% for easy YCB objects and 81.3% for difficult YCB objects while using a precise grasp with a two-soft-finger gripper to collect training data and power grasp with a four-soft-finger gripper to test the grasp policy.},
note = {Dual-track Submission with RAL: https://doi.org/10.1109/lra.2020.2969932},
keywords = {Authorship - Corresponding, Conf - ICRA, Special - Dual-Track},
pubstate = {published},
tppubtype = {conference}
}
Robot learning is widely accepted by academia and industry with its potentials to transform autonomous robot control through machine learning. Inspired by widely used soft fingers on grasping, we propose a method of rigid-soft interactive learning, aiming at reducing the time of data collection. In this letter, we classify the interaction categories into Rigid-Rigid, Rigid-Soft, SoftRigid according to the interaction surface between grippers and target objects. We find experimental evidence that the interaction types between grippers and target objects play an essential role in the learning methods. We use soft, stuffed toys for training, instead of everyday objects, to reduce the integration complexity and computational burden. Although the stuffed toys are limited in reflecting the physics of finger-object interaction in real-life scenarios, we exploit such rigid-soft interaction by changing the gripper fingers to the soft ones when dealing with rigid, daily-life items such as the Yale-CMU-Berkeley (YCB) objects. With a small data collection of 5 K picking attempts in total, our results suggest that such Rigid-Soft and Soft-Rigid interactions are transferable. Moreover, the combination of such interactions shows better performance on the grasping test. We also explore the effect of the grasp type on the learning method by changing the gripper configurations. We achieve the best grasping performance at 97.5% for easy YCB objects and 81.3% for difficult YCB objects while using a precise grasp with a two-soft-finger gripper to collect training data and power grasp with a four-soft-finger gripper to test the grasp policy.
16.
Zeyi Yang, Sheng Ge, Fang Wan, Yujia Liu, Chaoyang Song
Scalable Tactile Sensing for an Omni-adaptive Soft Robot Finger Conference
IEEE International Conference on Soft Robotics (RoboSoft2020), New Haven, CT, USA, 2020.
Links | BibTeX | Tags: Authorship - Corresponding, Conf - RoboSoft
@conference{Yang2020ScalableTactile,
title = {Scalable Tactile Sensing for an Omni-adaptive Soft Robot Finger},
author = {Zeyi Yang and Sheng Ge and Fang Wan and Yujia Liu and Chaoyang Song},
doi = {10.1109/robosoft48309.2020.9116026},
year = {2020},
date = {2020-05-15},
urldate = {2020-05-15},
booktitle = {IEEE International Conference on Soft Robotics (RoboSoft2020)},
address = {New Haven, CT, USA},
keywords = {Authorship - Corresponding, Conf - RoboSoft},
pubstate = {published},
tppubtype = {conference}
}
15.
Xia Wu, Haiyuan Liu, Ziqi Liu, Mingdong Chen, Fang Wan, Chenglong Fu, Harry Asada, Zheng Wang, Chaoyang Song
Robotic Cane as a Soft SuperLimb for Elderly Sit-to-Stand Assistance Conference
IEEE International Conference on Soft Robotics (RoboSoft2020), New Haven, CT, USA, 2020.
Links | BibTeX | Tags: Authorship - Corresponding, Conf - RoboSoft
@conference{Wu2020RoboticCane,
title = {Robotic Cane as a Soft SuperLimb for Elderly Sit-to-Stand Assistance},
author = {Xia Wu and Haiyuan Liu and Ziqi Liu and Mingdong Chen and Fang Wan and Chenglong Fu and Harry Asada and Zheng Wang and Chaoyang Song},
doi = {10.1109/robosoft48309.2020.9116028},
year = {2020},
date = {2020-05-15},
urldate = {2020-05-15},
booktitle = {IEEE International Conference on Soft Robotics (RoboSoft2020)},
address = {New Haven, CT, USA},
keywords = {Authorship - Corresponding, Conf - RoboSoft},
pubstate = {published},
tppubtype = {conference}
}
14.
Fang Wan, Haokun Wang, Jiyuan Wu, Yujia Liu, Sheng Ge, Chaoyang Song
A Reconfigurable Design for Omni-Adaptive Grasp Learning Conference
IEEE International Conference on Soft Robotics (RoboSoft2020), New Haven, CT, USA, 2020, (Dual-track Submission with RAL: https://doi.org/10.1109/lra.2020.2982059).
Abstract | Links | BibTeX | Tags: Authorship - Corresponding, Conf - RoboSoft, Special - Dual-Track
@conference{Wan2020ReconfigurableDesign,
title = {A Reconfigurable Design for Omni-Adaptive Grasp Learning},
author = {Fang Wan and Haokun Wang and Jiyuan Wu and Yujia Liu and Sheng Ge and Chaoyang Song},
doi = {10.1109/LRA.2020.2982059},
year = {2020},
date = {2020-05-15},
urldate = {2020-05-15},
booktitle = {IEEE International Conference on Soft Robotics (RoboSoft2020)},
address = {New Haven, CT, USA},
abstract = {The engineering design of robotic grippers presents an ample design space for optimization towards robust grasping. In this letter, we investigate how learning method can be used to support the design reconfiguration of robotic grippers for grasping using a novel soft structure with omni-directional adaptation. We propose a gripper system that is reconfigurable in terms of the number and arrangement of the proposed finger, which generates a large number of possible design configurations. Such design reconfigurations with omni-adaptive fingers enables us to systematically investigate the optimal arrangement of the fingers towards robust grasping. Furthermore, we adopt a learning-based method as the baseline to benchmark the effectiveness of each design configuration. As a result, we found that the 3-finger radial configuration is suitable for space-saving and cost-effectiveness, achieving an average 96% grasp success rate on seen and novel objects selected from the YCB dataset. The 4-finger radial arrangement can be applied to cases that require a higher payload with even distribution. We achieved dimension reduction using the radial gripper design with the removal of z-axis rotation during grasping. We also reported the different outcomes with or without friction enhancement of the soft finger network.},
note = {Dual-track Submission with RAL: https://doi.org/10.1109/lra.2020.2982059},
keywords = {Authorship - Corresponding, Conf - RoboSoft, Special - Dual-Track},
pubstate = {published},
tppubtype = {conference}
}
The engineering design of robotic grippers presents an ample design space for optimization towards robust grasping. In this letter, we investigate how learning method can be used to support the design reconfiguration of robotic grippers for grasping using a novel soft structure with omni-directional adaptation. We propose a gripper system that is reconfigurable in terms of the number and arrangement of the proposed finger, which generates a large number of possible design configurations. Such design reconfigurations with omni-adaptive fingers enables us to systematically investigate the optimal arrangement of the fingers towards robust grasping. Furthermore, we adopt a learning-based method as the baseline to benchmark the effectiveness of each design configuration. As a result, we found that the 3-finger radial configuration is suitable for space-saving and cost-effectiveness, achieving an average 96% grasp success rate on seen and novel objects selected from the YCB dataset. The 4-finger radial arrangement can be applied to cases that require a higher payload with even distribution. We achieved dimension reduction using the radial gripper design with the removal of z-axis rotation during grasping. We also reported the different outcomes with or without friction enhancement of the soft finger network.
13.
Xinyao Hu, Chuang Luo, Hao Li, Liyao Jia, Chaoyang Song, Zheng Wang, Xingda Qu
An Ankle Based Soft Active Orthotic Device Powered by Pneumatic Artificial Muscle Conference
IEEE International Conference on Real-time Computing and Robotics (RCAR2019), Irkutsk, Russia, 2019.
Links | BibTeX | Tags: Authorship - Co-Author, Conf - RCAR
@conference{Hu2019AnAnkle,
title = {An Ankle Based Soft Active Orthotic Device Powered by Pneumatic Artificial Muscle},
author = {Xinyao Hu and Chuang Luo and Hao Li and Liyao Jia and Chaoyang Song and Zheng Wang and Xingda Qu},
doi = {10.1109/RCAR47638.2019.9043948},
year = {2019},
date = {2019-08-04},
urldate = {2019-08-04},
booktitle = {IEEE International Conference on Real-time Computing and Robotics (RCAR2019)},
address = {Irkutsk, Russia},
keywords = {Authorship - Co-Author, Conf - RCAR},
pubstate = {published},
tppubtype = {conference}
}
12.
Xiaojiao Chen, Tommy Hu, Chaoyang Song, Zheng Wang
Analytical Solution to Global Dynamic Balance Control of the Acrobot Conference
IEEE International Conference on Real-time Computing and Robotics (RCAR2018), Kandima, Maldives, 2018.
Links | BibTeX | Tags: Authorship - Co-Author, Conf - RCAR
@conference{Chen2018AnalyticalSolution,
title = {Analytical Solution to Global Dynamic Balance Control of the Acrobot},
author = {Xiaojiao Chen and Tommy Hu and Chaoyang Song and Zheng Wang},
doi = {10.1109/rcar.2018.8621827},
year = {2018},
date = {2018-08-01},
urldate = {2018-08-01},
booktitle = {IEEE International Conference on Real-time Computing and Robotics (RCAR2018)},
address = {Kandima, Maldives},
keywords = {Authorship - Co-Author, Conf - RCAR},
pubstate = {published},
tppubtype = {conference}
}
11.
Fang Wan, Zheng Wang, Brooke Franchuk, Xinyao Hu, Zhenglong Sun, Chaoyang Song
Hybrid Actuator Design for a Gait Augmentation Wearable Conference
IEEE International Conference on Robotics and Biomimetics (ROBIO2017), Macau, 2017.
Links | BibTeX | Tags: Authorship - Corresponding, Conf - ROBIO
@conference{Wan2017HybridActuator,
title = {Hybrid Actuator Design for a Gait Augmentation Wearable},
author = {Fang Wan and Zheng Wang and Brooke Franchuk and Xinyao Hu and Zhenglong Sun and Chaoyang Song},
doi = {10.1109/robio.2017.8324761},
year = {2017},
date = {2017-12-05},
urldate = {2017-12-05},
booktitle = {IEEE International Conference on Robotics and Biomimetics (ROBIO2017)},
address = {Macau},
keywords = {Authorship - Corresponding, Conf - ROBIO},
pubstate = {published},
tppubtype = {conference}
}
10.
Yaohui Chen, Sing Le, Qiao Chu Tan, Oscar Lau, Chaoyang Song
A Lobster-Inspired Hybrid Actuator with Rigid and Soft Components Conference
ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (DETC/CIE2017), Cleveland, Ohio, USA, 2017.
Links | BibTeX | Tags: Authorship - Corresponding, Conf - DETC/CIE
@conference{Chen2017ALobsterDETC,
title = {A Lobster-Inspired Hybrid Actuator with Rigid and Soft Components},
author = {Yaohui Chen and Sing Le and Qiao Chu Tan and Oscar Lau and Chaoyang Song},
doi = {10.1115/detc2017-68082},
year = {2017},
date = {2017-08-16},
urldate = {2017-08-16},
booktitle = {ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (DETC/CIE2017)},
address = {Cleveland, Ohio, USA},
keywords = {Authorship - Corresponding, Conf - DETC/CIE},
pubstate = {published},
tppubtype = {conference}
}
9.
Yaohui Chen, Sing Le, Qiao Chu Tan, Oscar Lau, Fang Wan, Chaoyang Song
A Reconfigurable Hybrid Actuator with Rigid and Soft Components Conference
IEEE International Conference on Robotics and Automation (ICRA2017), Marina Bay Sands, Singapore, 2017.
Links | BibTeX | Tags: Authorship - Corresponding, Conf - ICRA
@conference{Chen2017AReconfigurable,
title = {A Reconfigurable Hybrid Actuator with Rigid and Soft Components},
author = {Yaohui Chen and Sing Le and Qiao Chu Tan and Oscar Lau and Fang Wan and Chaoyang Song},
doi = {10.1109/icra.2017.7988691},
year = {2017},
date = {2017-05-29},
urldate = {2017-05-29},
booktitle = {IEEE International Conference on Robotics and Automation (ICRA2017)},
address = {Marina Bay Sands, Singapore},
keywords = {Authorship - Corresponding, Conf - ICRA},
pubstate = {published},
tppubtype = {conference}
}
8.
Yaohui Chen, Sing Le, Qiao Chu Tan, Oscar Lau, Fang Wan, Chaoyang Song
A Lobster-inspired Robotic Glove for Hand Rehabilitation Conference
IEEE International Conference on Robotics and Automation (ICRA2017), Marina Bay Sands, Singapore, 2017.
Links | BibTeX | Tags: Authorship - Corresponding, Conf - ICRA
@conference{Chen2017ALobsterICRA,
title = {A Lobster-inspired Robotic Glove for Hand Rehabilitation},
author = {Yaohui Chen and Sing Le and Qiao Chu Tan and Oscar Lau and Fang Wan and Chaoyang Song},
doi = {10.1109/icra.2017.7989556},
year = {2017},
date = {2017-05-29},
urldate = {2017-05-29},
booktitle = {IEEE International Conference on Robotics and Automation (ICRA2017)},
address = {Marina Bay Sands, Singapore},
keywords = {Authorship - Corresponding, Conf - ICRA},
pubstate = {published},
tppubtype = {conference}
}
7.
Juan Yi, Zhong Shen, Chaoyang Song, Zheng Wang
A Soft Robotic Glove for Hand Motion Assistance Honorable Mention Conference
IEEE International Conference on Real-time Computing and Robotics (RCAR2016), Angkor Wat, Cambodia, 2016.
Links | BibTeX | Tags: Authorship - Co-Author, Award - Best Conference Paper Finalist, Award - Paper, Conf - RCAR
@conference{Yi2016ASoft,
title = {A Soft Robotic Glove for Hand Motion Assistance},
author = {Juan Yi and Zhong Shen and Chaoyang Song and Zheng Wang},
doi = {10.1109/rcar.2016.7784010},
year = {2016},
date = {2016-06-06},
urldate = {2016-06-06},
booktitle = {IEEE International Conference on Real-time Computing and Robotics (RCAR2016)},
address = {Angkor Wat, Cambodia},
keywords = {Authorship - Co-Author, Award - Best Conference Paper Finalist, Award - Paper, Conf - RCAR},
pubstate = {published},
tppubtype = {conference}
}
6.
Chaoyang Song, Jianxi Luo, Katja Hölttä-Otto, Kevin Otto, Warren Seering
The Design of Crowd-Funded Products Conference
ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (DETC/CIE2015), Boston, Massachusetts, USA, 2015.
Links | BibTeX | Tags: Authorship - First Author, Conf - DETC/CIE
@conference{Song2015TheDesign,
title = {The Design of Crowd-Funded Products},
author = {Chaoyang Song and Jianxi Luo and Katja Hölttä-Otto and Kevin Otto and Warren Seering},
doi = {10.1115/detc2015-46917},
year = {2015},
date = {2015-08-02},
urldate = {2015-08-02},
booktitle = {ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (DETC/CIE2015)},
address = {Boston, Massachusetts, USA},
keywords = {Authorship - First Author, Conf - DETC/CIE},
pubstate = {published},
tppubtype = {conference}
}
5.
Chaoyang Song, Jianxi Luo, Katja Hölttä-Otto, Kevin Otto, Warren Seering
Risk and Innovation Balance in Crowdfunding New Products Honorable Mention Conference
International Conference on Engineering Design (ICED2015), Milan, Italy, 2015.
Abstract | Links | BibTeX | Tags: Authorship - First Author, Award - Paper, Award - Reviewers' Favorites Award, Conf - ICED
@conference{Song2015RiskAnd,
title = {Risk and Innovation Balance in Crowdfunding New Products},
author = {Chaoyang Song and Jianxi Luo and Katja Hölttä-Otto and Kevin Otto and Warren Seering},
url = {https://www.designsociety.org/publication/37919/risk+and+innovation+balance+in+crowdfunding+new+products},
year = {2015},
date = {2015-07-27},
urldate = {2015-07-27},
booktitle = {International Conference on Engineering Design (ICED2015)},
address = {Milan, Italy},
abstract = {Many have considered that innovation through new and small companies is a vital driver for sustainable economic growth. Recent growth in Web 2.0 demands small companies to further incorporate risk management while developing innovative products. How to balance risk and innovation during new product development becomes a priority for small companies to survive the competition. Yet, the approach is not likely similar to that employed by incumbent firms. This paper explores innovation versus risk for small companies using crowdfunding products as a proxy for analysis. A database with 127 consumer electronics, namely 3D printers and smart watches, are collected from Kickstarter and Indiegogo. The metric of Real-Win-Worth is adapted to provide a well-rounded assessment of the product’s innovation, risk and other related business and engineering aspects. Our result suggests a preliminary framework of innovation and risk balance for crowdfunding NPD success. A statistical model is developed to correlate the amount of crowdfunding raised with 64% predictability. These results may contribute to better understand and balance risk and innovation in crowdfunding and small company contexts.},
keywords = {Authorship - First Author, Award - Paper, Award - Reviewers' Favorites Award, Conf - ICED},
pubstate = {published},
tppubtype = {conference}
}
Many have considered that innovation through new and small companies is a vital driver for sustainable economic growth. Recent growth in Web 2.0 demands small companies to further incorporate risk management while developing innovative products. How to balance risk and innovation during new product development becomes a priority for small companies to survive the competition. Yet, the approach is not likely similar to that employed by incumbent firms. This paper explores innovation versus risk for small companies using crowdfunding products as a proxy for analysis. A database with 127 consumer electronics, namely 3D printers and smart watches, are collected from Kickstarter and Indiegogo. The metric of Real-Win-Worth is adapted to provide a well-rounded assessment of the product’s innovation, risk and other related business and engineering aspects. Our result suggests a preliminary framework of innovation and risk balance for crowdfunding NPD success. A statistical model is developed to correlate the amount of crowdfunding raised with 64% predictability. These results may contribute to better understand and balance risk and innovation in crowdfunding and small company contexts.
4.
Chaoyang Song, Jianxi Luo, Katja Hölttä-Otto, Kevin Otto
Product Innovation Differences between New Ventures and Incumbent Firms Conference
Annual Meeting of the Academy of Management (AoM2014), Philadelphia, PA, USA, 2014.
Links | BibTeX | Tags: Authorship - Corresponding, Authorship - First Author, Conf - AoM
@conference{Song2014ProductInnovation,
title = {Product Innovation Differences between New Ventures and Incumbent Firms},
author = {Chaoyang Song and Jianxi Luo and Katja Hölttä-Otto and Kevin Otto},
doi = {10.5465/ambpp.2014.13204abstract},
year = {2014},
date = {2014-08-01},
urldate = {2014-08-01},
booktitle = {Annual Meeting of the Academy of Management (AoM2014)},
address = {Philadelphia, PA, USA},
keywords = {Authorship - Corresponding, Authorship - First Author, Conf - AoM},
pubstate = {published},
tppubtype = {conference}
}
3.
Chaoyang Song, Yan Chen, I-Ming Chen
Bifurcation Behavior of the Line-Symmetric Bricard Linkage without Offsets Conference
IFToMM International Symposium on Robotics and Mechatronics (ISRM2013), Singapore, 2013.
Links | BibTeX | Tags: Authorship - First Author, Conf - ISRM
@conference{Song2013BirfurcationBehavior,
title = {Bifurcation Behavior of the Line-Symmetric Bricard Linkage without Offsets},
author = {Chaoyang Song and Yan Chen and I-Ming Chen},
doi = {10.3850/978-981-07-7744-9_064},
year = {2013},
date = {2013-10-02},
urldate = {2013-10-02},
booktitle = {IFToMM International Symposium on Robotics and Mechatronics (ISRM2013)},
address = {Singapore},
keywords = {Authorship - First Author, Conf - ISRM},
pubstate = {published},
tppubtype = {conference}
}
2.
Chaoyang Song, Yan Chen
A Special Wohlharts Double-Goldberg 6R Linkage and Its Multiple Operation Forms among 4R and 6R Linkages Honorable Mention Conference
ASME/IEEE International Conference on Reconfigurable Mechanisms and Robots (ReMAR2012), Tianjin, China, 2012.
Links | BibTeX | Tags: Authorship - First Author, Award - Travel Grant, Award - Young Delegate Travel Grant, Conf - ReMAR
@conference{Song2012ASpecial,
title = {A Special Wohlharts Double-Goldberg 6R Linkage and Its Multiple Operation Forms among 4R and 6R Linkages},
author = {Chaoyang Song and Yan Chen},
doi = {10.1007/978-1-4471-4141-9_5},
year = {2012},
date = {2012-07-09},
urldate = {2012-07-09},
booktitle = {ASME/IEEE International Conference on Reconfigurable Mechanisms and Robots (ReMAR2012)},
address = {Tianjin, China},
keywords = {Authorship - First Author, Award - Travel Grant, Award - Young Delegate Travel Grant, Conf - ReMAR},
pubstate = {published},
tppubtype = {conference}
}
1.
Chaoyang Song, Yan Chen
The Original Double-Goldberg 6R Linkage and its Bifurcation Analysis Conference
IFToMM International Symposium on Multibody Systems and Mechatronics (MuSME2011), Valencia, Spain, 2011.
Links | BibTeX | Tags: Authorship - First Author, Conf - MuSME
@conference{Song2011TheOriginal,
title = {The Original Double-Goldberg 6R Linkage and its Bifurcation Analysis},
author = {Chaoyang Song and Yan Chen},
doi = {10.13140/2.1.2041.3764},
year = {2011},
date = {2011-10-25},
urldate = {2011-10-25},
booktitle = {IFToMM International Symposium on Multibody Systems and Mechatronics (MuSME2011)},
address = {Valencia, Spain},
keywords = {Authorship - First Author, Conf - MuSME},
pubstate = {published},
tppubtype = {conference}
}
Extended Abstracts
5.
Haoran Sun, Linhan Yang, Zeqing Zhang, Ning Guo, Lei Yang, Fang Wan, Chaoyang Song, Jia Pan
CopGNN: Learning End-to-End Cloth Coverage Prediction via Graph Neural Networks Workshop
2024, (Extended Abstract accepted to IROS 2024 Workshop on Benchmarking via Competitions in Robotic Grasping and Manipulation).
@workshop{Sun2024CopGNN,
title = {CopGNN: Learning End-to-End Cloth Coverage Prediction via Graph Neural Networks},
author = {Haoran Sun and Linhan Yang and Zeqing Zhang and Ning Guo and Lei Yang and Fang Wan and Chaoyang Song and Jia Pan},
url = {https://sites.google.com/view/iros2024-workshop-bench-in-rgm/},
year = {2024},
date = {2024-10-13},
urldate = {2024-10-13},
abstract = {Cloth manipulation in robotics, such as folding or unfolding fabrics, remains challenging due to deformable materials' complex and nonlinear dynamics, which can adopt infinite configurations. As Team Greater Bay, we participated in the ICRA 2024 Cloth Competition and scored an Average Coverage of 0.53 (the 1st place team scored 0.60). This extended abstract presents our Coverage Prediction Graph Neural Network (CopGNN) approach implemented for this competition. Instead of directly estimating the cloth's configuration, our method implicitly infers the unknown state using a Graph Neural Network (GNN). It predicts the resultant coverage area from multiple grasping points using a second GNN without relying on an explicit dynamics model. Contributions of this work include: (1) Developed a comprehensive simulation pipeline to generate a large-scale dataset tailored to the cloth manipulation task. (2) Proposed an end-to-end approach to predict the coverage area using only the hanging cloth's depth image. (3) Introduced a heuristic-based sampling strategy to enhance the robustness of zero-shot sim-to-real transfer.},
note = {Extended Abstract accepted to IROS 2024 Workshop on Benchmarking via Competitions in Robotic Grasping and Manipulation},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
Cloth manipulation in robotics, such as folding or unfolding fabrics, remains challenging due to deformable materials' complex and nonlinear dynamics, which can adopt infinite configurations. As Team Greater Bay, we participated in the ICRA 2024 Cloth Competition and scored an Average Coverage of 0.53 (the 1st place team scored 0.60). This extended abstract presents our Coverage Prediction Graph Neural Network (CopGNN) approach implemented for this competition. Instead of directly estimating the cloth's configuration, our method implicitly infers the unknown state using a Graph Neural Network (GNN). It predicts the resultant coverage area from multiple grasping points using a second GNN without relying on an explicit dynamics model. Contributions of this work include: (1) Developed a comprehensive simulation pipeline to generate a large-scale dataset tailored to the cloth manipulation task. (2) Proposed an end-to-end approach to predict the coverage area using only the hanging cloth's depth image. (3) Introduced a heuristic-based sampling strategy to enhance the robustness of zero-shot sim-to-real transfer.
4.
Tianyu Wu, Sheng Ge, Yujian Dong, Ronghan Xu, Fang Wan, Chaoyang Song
From DeepClaw to MagiClaw: Towards Universal Action Embodiment Workshop
2024, (Extended Abstract accepted to IROS 2024 Workshop on Environment Dynamics Matters: Embodied Navigation to Movable Objects).
@workshop{Wu2024MagiClaw,
title = {From DeepClaw to MagiClaw: Towards Universal Action Embodiment},
author = {Tianyu Wu and Sheng Ge and Yujian Dong and Ronghan Xu and Fang Wan and Chaoyang Song},
url = {https://edmws.github.io/},
year = {2024},
date = {2024-10-13},
urldate = {2024-10-13},
note = {Extended Abstract accepted to IROS 2024 Workshop on Environment Dynamics Matters: Embodied Navigation to Movable Objects},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
3.
Chaoyang Song
The Design and Learning of Overconstrained Mechanisms towards Overconstrained Robotics Workshop
Mechanism and Machine Theory Symposium, Guimarães, Portugal, 2024, (Extended Abstract accepted to Mechanism and Machine Theory Symposium).
@workshop{Song2024TheDesign,
title = {The Design and Learning of Overconstrained Mechanisms towards Overconstrained Robotics},
author = {Chaoyang Song},
url = {https://mmtsymposium.com/
https://iftomm-world.org/conferences/mmt-symposium/#:~:text=The%20MMT%20Symposium%20will%20be%20held%20in%20Guimar%C3%A3es%20-%20Portugal,},
year = {2024},
date = {2024-06-26},
urldate = {2024-06-26},
booktitle = {Mechanism and Machine Theory Symposium},
address = {Guimarães, Portugal},
abstract = {Overconstrained mechanisms play a pivotal role in mechanism theory, combining mathematical science with engineering design to provide the foundational kinematics for emerging applications in modern machinery and robotic systems. Calculating a mechanism’s mobility is among an engineer’s first steps towards building machines as desired, which can be a challenging task. The paradox of overconstrained mechanisms is, quoting Prof. Andreas Müller, that “although one may not construct a ‘perfectly overconstrained’ mechanism, one will, and this is the design goal, end up with an ‘almost overconstrained’ mechanism ... Therefore, and due to the flexibility of links and joint clearances, the real mechanism will exhibit almost the type of motion of its perfect (overconstrained) prototype. This is why the understanding of overconstrained mechanisms is important though.” The Mechanism and Machine Theory is the leading platform attracting researchers contributing to this research topic. However, a long-standing challenge remains to push the overconstrained mechanisms from theoretical kinematics to engineering applications with advanced robotics, where an emerging field of “overconstrained robotics” may interest researchers in related fields of expertise.},
note = {Extended Abstract accepted to Mechanism and Machine Theory Symposium},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
Overconstrained mechanisms play a pivotal role in mechanism theory, combining mathematical science with engineering design to provide the foundational kinematics for emerging applications in modern machinery and robotic systems. Calculating a mechanism’s mobility is among an engineer’s first steps towards building machines as desired, which can be a challenging task. The paradox of overconstrained mechanisms is, quoting Prof. Andreas Müller, that “although one may not construct a ‘perfectly overconstrained’ mechanism, one will, and this is the design goal, end up with an ‘almost overconstrained’ mechanism ... Therefore, and due to the flexibility of links and joint clearances, the real mechanism will exhibit almost the type of motion of its perfect (overconstrained) prototype. This is why the understanding of overconstrained mechanisms is important though.” The Mechanism and Machine Theory is the leading platform attracting researchers contributing to this research topic. However, a long-standing challenge remains to push the overconstrained mechanisms from theoretical kinematics to engineering applications with advanced robotics, where an emerging field of “overconstrained robotics” may interest researchers in related fields of expertise.
2.
Jianwen Luo, Sicong Liu, Chengyu Lin, Yong Zhou, Zixuan Fan, Zheng Wang, Chaoyang Song, Harry Asada, Chenglong Fu
Mapping Human Muscle Force to Supernumerary Robotics Device for Overhead Task Assistance Workshop
2020, (Extended Abstract accepted to the IEEE/ASME AIM 2020 Workshop on Supernumerary Robotic Devices).
@workshop{Luo2020MappingHuman,
title = {Mapping Human Muscle Force to Supernumerary Robotics Device for Overhead Task Assistance},
author = {Jianwen Luo and Sicong Liu and Chengyu Lin and Yong Zhou and Zixuan Fan and Zheng Wang and Chaoyang Song and Harry Asada and Chenglong Fu},
url = {https://aim2020srd.wixsite.com/aim2020srd},
doi = {10.48550/arXiv.2107.13799},
year = {2020},
date = {2020-11-01},
urldate = {2020-11-01},
note = {Extended Abstract accepted to the IEEE/ASME AIM 2020 Workshop on Supernumerary Robotic Devices},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
1.
Xiaobo Liu, Fang Wan, Haoran Sun, Qichen Luo, Wei Zhang, Chaoyang Song
Jigsaw-based Benchmarking for Learning Robotic Manipulation Workshop
2019, (Invited Presentation at ICRA 2019 Workshop on Benchmarks for Robotic Manipulation).
@workshop{Liu2019Jigsaw,
title = {Jigsaw-based Benchmarking for Learning Robotic Manipulation},
author = {Xiaobo Liu and Fang Wan and Haoran Sun and Qichen Luo and Wei Zhang and Chaoyang Song},
url = {https://www.ycbbenchmarks.com/ICRA2019_workshop},
year = {2019},
date = {2019-05-23},
urldate = {2019-05-23},
abstract = {Benchmarking provides experimental evidence of the scientific baseline to enhance the progression of fundamental research, which is also applicable to robotics. In this paper, we propose a method to benchmark metrics of robotic manipulation using the jigsaw game to address the spatial-temporal reasoning skills for robot learning. In particular, our method exploits a simple set of jigsaw pieces by designing a structured protocol following a typical, functional implementation of robot manipulation, which is also highly customizable according to a wide range of task specifications. Researchers interested in understanding the performances of their hardware, software, and integration can selectively adopt the proposed protocol to benchmark their research outputs on a comparable scale in the functional, task and system level of details. The purpose is to provide a potential look-up table for learning-based robot manipulation, which is commonly available in other established engineering disciplines, to facilitate the adoption of robotics through calculated, empirical, and systematic experimental evidence.},
note = {Invited Presentation at ICRA 2019 Workshop on Benchmarks for Robotic Manipulation},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
Benchmarking provides experimental evidence of the scientific baseline to enhance the progression of fundamental research, which is also applicable to robotics. In this paper, we propose a method to benchmark metrics of robotic manipulation using the jigsaw game to address the spatial-temporal reasoning skills for robot learning. In particular, our method exploits a simple set of jigsaw pieces by designing a structured protocol following a typical, functional implementation of robot manipulation, which is also highly customizable according to a wide range of task specifications. Researchers interested in understanding the performances of their hardware, software, and integration can selectively adopt the proposed protocol to benchmark their research outputs on a comparable scale in the functional, task and system level of details. The purpose is to provide a potential look-up table for learning-based robot manipulation, which is commonly available in other established engineering disciplines, to facilitate the adoption of robotics through calculated, empirical, and systematic experimental evidence.
Doctoral Thesis
5.
Yuping Gu
Computational Design and Energy-Efficient Optimization of Overconstrained Robotic Limbs PhD Thesis
Southern University of Science and Technology & The University of Hong Kong, 2024.
@phdthesis{Gu2024PhDThesis,
title = {Computational Design and Energy-Efficient Optimization of Overconstrained Robotic Limbs},
author = {Yuping Gu},
year = {2024},
date = {2024-09-06},
urldate = {2024-09-06},
school = {Southern University of Science and Technology & The University of Hong Kong},
abstract = {Energy efficiency is one of the key evaluating indicators for legged robots, which is also the driving factor in biological structure evolution. Lots of efforts have been made to develop legged machines with agile and energy-efficient motion like animals. By leveraging the planar four-bar or its variations, modern robotic design can reduce energy consumption with negligible leg inertia, which has been a widely adopted design pattern, but remains a limited adoption of overconstrained linkages in robotic application, even though it is a class of simplest revolute-only spatial mechanism. On the other hand, most of the existing legged robots still make significant trade-offs among various design indices, and there is an open question of which limb configuration has the best performance across energy efficiency, versatility, and mechanical robustness. This thesis builds upon the theoretical foundations, design principles, and optimization strategies of a class of novel robotic limb designs based on overconstrained linkage, towards developing advanced robotic limbs with better performance in energy efficiency. The first part of this thesis (Chapter 2) focuses on the kinematic derivation and engineering application of overconstrained robotic limbs, as well as the investigation of their spatial characteristics. The proposed prototype quadruped was capable of omni-directional locomotion and had a minimal turning radius (0.2 Body Length) using the fewest actuators. The second part (Chapter 3) develops a computational optimization framework for optimizing the energy efficiency performance of generalized robotic limb design. The framework is validated by hardware experiment using a reconfigurable quadruped prototype and empirically validated the outstanding performance of overconstrained robotic limbs in omni-directional locomotion. The third part (Chapter 4) deepens the findings in the above studies and proposes a computational framework to design 1-DoF overconstrained robotic limbs for desired spatial trajectory while achieving energy-efficient, self-collision-free motion in full-cycle rotations. The resulting hexapod robot with overconstrained robotic limbs showed state-of-the-art energy efficiency compared with other small hexapod robots in recent years. The findings of this research argue the potential for a research field in overconstrained robotics by using overconstrained linkages to formulate novel robot structures.},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
Energy efficiency is one of the key evaluating indicators for legged robots, which is also the driving factor in biological structure evolution. Lots of efforts have been made to develop legged machines with agile and energy-efficient motion like animals. By leveraging the planar four-bar or its variations, modern robotic design can reduce energy consumption with negligible leg inertia, which has been a widely adopted design pattern, but remains a limited adoption of overconstrained linkages in robotic application, even though it is a class of simplest revolute-only spatial mechanism. On the other hand, most of the existing legged robots still make significant trade-offs among various design indices, and there is an open question of which limb configuration has the best performance across energy efficiency, versatility, and mechanical robustness. This thesis builds upon the theoretical foundations, design principles, and optimization strategies of a class of novel robotic limb designs based on overconstrained linkage, towards developing advanced robotic limbs with better performance in energy efficiency. The first part of this thesis (Chapter 2) focuses on the kinematic derivation and engineering application of overconstrained robotic limbs, as well as the investigation of their spatial characteristics. The proposed prototype quadruped was capable of omni-directional locomotion and had a minimal turning radius (0.2 Body Length) using the fewest actuators. The second part (Chapter 3) develops a computational optimization framework for optimizing the energy efficiency performance of generalized robotic limb design. The framework is validated by hardware experiment using a reconfigurable quadruped prototype and empirically validated the outstanding performance of overconstrained robotic limbs in omni-directional locomotion. The third part (Chapter 4) deepens the findings in the above studies and proposes a computational framework to design 1-DoF overconstrained robotic limbs for desired spatial trajectory while achieving energy-efficient, self-collision-free motion in full-cycle rotations. The resulting hexapod robot with overconstrained robotic limbs showed state-of-the-art energy efficiency compared with other small hexapod robots in recent years. The findings of this research argue the potential for a research field in overconstrained robotics by using overconstrained linkages to formulate novel robot structures.
4.
Xiaobo Liu
Southern University of Science and Technology, 2024.
@phdthesis{Liu2024PhDThesis,
title = {Proprioceptive Sensing of Soft Polyhedral Networks and Robot Manipulation Learning Based on Visual Methods},
author = {Xiaobo Liu},
year = {2024},
date = {2024-08-29},
urldate = {2024-08-29},
school = {Southern University of Science and Technology},
abstract = {Object manipulation is the most fundamental ability for robots to interact with objects. The robot gripper, as the primary physical interface with objects, directly impacts the robot's flexibility and efficiency in various application scenarios. Compared to traditional rigid grippers, soft grippers have higher flexibility, environmental adaptability, and human-machine interaction capabilities. The soft gripper can achieve stable grasping of objects with different shapes and damage-free handling of fragile objects, making them widely used in robot grasping and manipulation scenarios. However, their underactuation characteristic makes the control and perception of soft fingers more complex and challenging. This thesis focuses on proprioceptive and object perception based on soft fingers, proposing a design method for a class of omni-directional adaptive soft fingers called Soft Polyhedral Networks. This thesis addresses issues such as viscoelasticity, proprioception, robotic manipulation benchmark, and estimation of object poses in hand. By integrating ArUco markers and cameras into fingers, this thesis studies the proprioception with finger deformation. The soft finger is modeled and simulated with the Abaqus, and the simulation deformation data is collected. An MLP network is trained to reconstruct finger deformation from the deformation data, and transferred to actual physical interactions, demonstrating its Sim2Real capability. The static and dynamic viscoelastic characteristics are further analyzed. Compared to existing soft sensors, the model which considering viscoelastic characteristics has higher force prediction accuracy. To address the issue of uncertainty in the state of objects during manipulation, this thesis propose a method to estimating pose and category of in hand objects based on soft fingers and in-finger vision. This method is suitable for different types of grippers and tactile sensors and was tested on a two-finger gripper, achieving high classification and positioning accuracy. To solve the data generation and standardization problem in learning manipulation tasks, a benchmark experimental system for robot manipulation tasks is proposed, and a robot operation data collection platform called DeepClaw is bulit, which is shareable and reproducible. DeepClaw enables low-cost collection of manipulation trajectories and interaction forces, and a manipulation task dataset is created. In the context of object manipulation, by observing the deformation of the soft fingers, they were endowed with the ability to sense the contact state. This enabled the successful realization of real-time contact force and deformation sensing, leading to real-time object state estimation. Finally, manipulation experiments validated the effectiveness of this sensing technology, providing a technical foundation for dexterous object manipulation.},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
Object manipulation is the most fundamental ability for robots to interact with objects. The robot gripper, as the primary physical interface with objects, directly impacts the robot's flexibility and efficiency in various application scenarios. Compared to traditional rigid grippers, soft grippers have higher flexibility, environmental adaptability, and human-machine interaction capabilities. The soft gripper can achieve stable grasping of objects with different shapes and damage-free handling of fragile objects, making them widely used in robot grasping and manipulation scenarios. However, their underactuation characteristic makes the control and perception of soft fingers more complex and challenging. This thesis focuses on proprioceptive and object perception based on soft fingers, proposing a design method for a class of omni-directional adaptive soft fingers called Soft Polyhedral Networks. This thesis addresses issues such as viscoelasticity, proprioception, robotic manipulation benchmark, and estimation of object poses in hand. By integrating ArUco markers and cameras into fingers, this thesis studies the proprioception with finger deformation. The soft finger is modeled and simulated with the Abaqus, and the simulation deformation data is collected. An MLP network is trained to reconstruct finger deformation from the deformation data, and transferred to actual physical interactions, demonstrating its Sim2Real capability. The static and dynamic viscoelastic characteristics are further analyzed. Compared to existing soft sensors, the model which considering viscoelastic characteristics has higher force prediction accuracy. To address the issue of uncertainty in the state of objects during manipulation, this thesis propose a method to estimating pose and category of in hand objects based on soft fingers and in-finger vision. This method is suitable for different types of grippers and tactile sensors and was tested on a two-finger gripper, achieving high classification and positioning accuracy. To solve the data generation and standardization problem in learning manipulation tasks, a benchmark experimental system for robot manipulation tasks is proposed, and a robot operation data collection platform called DeepClaw is bulit, which is shareable and reproducible. DeepClaw enables low-cost collection of manipulation trajectories and interaction forces, and a manipulation task dataset is created. In the context of object manipulation, by observing the deformation of the soft fingers, they were endowed with the ability to sense the contact state. This enabled the successful realization of real-time contact force and deformation sensing, leading to real-time object state estimation. Finally, manipulation experiments validated the effectiveness of this sensing technology, providing a technical foundation for dexterous object manipulation.
3.
Ning Guo
Southern University of Science and Technology, 2024.
@phdthesis{Guo2024PhDThesis,
title = {Soft Robotic Perception Mechanism via Vision-Based Tactile Reconstruction and its Amphibious Applications in Dexterous Manipulation},
author = {Ning Guo},
year = {2024},
date = {2024-08-29},
urldate = {2024-08-29},
school = {Southern University of Science and Technology},
abstract = {In recent years, with the rapid advancement of robotics technology, achieving dexterous manipulation in complex environments has become a focal point of research. Particularly in amphibious environments, robots need to possess high levels of perception and adaptability to cope with varying operational conditions. Against this backdrop, vision-based tactile technology, which integrates visual and tactile information, has shown great potential. This thesis focuses on the perceptual mechanisms of vision-based soft tactile sensing through deformation reconstruction and its application in dexterous manipulation of robots in amphibious environments. Soft robots can undergo continuous deformation when physically interacting with the external environment. These deformation characteristics of the soft structure body record rich tactile sensory information. Therefore, reconstructing the deformation of the soft structure in three-dimensional space is key to decoding tactile information. This paper establishes a multimodal sensing framework that couples the deformation energy of hyperelastic materials with the similarity of visual observation point features based on physical principles. Following the principle of minimizing total potential energy, the problem of solving the deformation field of hyperelastic medium in equilibrium state is formulated into a constrained optimization problem under visual observation. By constructing finite element spatial discretization and visual observation point constraints, an efficient numerical algorithm is proposed for real-time deformation reconstruction of the soft contact medium. To validate the effectiveness of the proposed perceptual mechanisms, this paper optimizes the hardware structure and integrates the visuotactile algorithm for a class of flexible fingers with omnidirectional adaptive capability, endowing passive robotic flexible fingers with active proprioceptive shape sensing and contact force distribution sensing capabilities. Simulation and experimental results demonstrate the real-time performance (<= 50 ms) and accuracy (<= 2.5 mm) of the proposed sensing method in reconstructing contact deformations of flexible fingers. The deformation state of the soft robotic fingers during contact is encoded in the form of visual image. However, it is extremely challenging to decode the three-dimensional deformation field of flexible structures from two-dimensional images to obtain tactile information. For complex cross-medium visual-tactile perception tasks, this paper proposes a supervised variational autoencoder learning framework to establish a visual-tactile cross-modal perception model. The deformation information of flexible fingers encoded by visual images and mechanical principles are jointly encoded and represented as interpretable latent variable features. These latent variable features are used to convert between visual and tactile modalities, resulting in a more generalizable visuotactile sensing mechanism. Experimental studies are carried out on datasets and amphibious environments, confirming the effectiveness and accuracy of the cross-medium inference model for vision-based tactile perception (R2 >= 0.98). The application research of visuotactile sensing information in robotic dexterous manipulation mainly includes adaptive grasping and environment exploration. However, applying visuotactile sensing technology in amphibious environments and studying robotic dexterous manipulation based on flexible visuotactile sensing is extremely rare. This paper studies the grasping performance of the flexible finger visuotactile sensing system in amphibious environments, establishing robotic adaptive grasping experimental platforms onland and underwater to verify the adaptability and superiority of the proposed sensing system in amphibious grasping operations. Additionally, for the study of the environment exploration sensing performance of the flexible finger visuotactile sensing system, experiments were completed on two-dimensional weld seam tracking in industrial scenarios and three-dimensional object shape sensing in underwater salvage scenarios, validating the accuracy and robustness of the proposed sensing system in amphibious environment exploration tasks. The proposed perceptual mechanisms of vision-based soft tactile sensing through deformation reconstruction provides theoretical guidance and design basis for the design of new visuotactile sensor hardware structures and sensing algorithms. The application of robotic dexterous manipulation in amphibious environments opens new research directions and application fields for visuotactile sensing technology.},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
In recent years, with the rapid advancement of robotics technology, achieving dexterous manipulation in complex environments has become a focal point of research. Particularly in amphibious environments, robots need to possess high levels of perception and adaptability to cope with varying operational conditions. Against this backdrop, vision-based tactile technology, which integrates visual and tactile information, has shown great potential. This thesis focuses on the perceptual mechanisms of vision-based soft tactile sensing through deformation reconstruction and its application in dexterous manipulation of robots in amphibious environments. Soft robots can undergo continuous deformation when physically interacting with the external environment. These deformation characteristics of the soft structure body record rich tactile sensory information. Therefore, reconstructing the deformation of the soft structure in three-dimensional space is key to decoding tactile information. This paper establishes a multimodal sensing framework that couples the deformation energy of hyperelastic materials with the similarity of visual observation point features based on physical principles. Following the principle of minimizing total potential energy, the problem of solving the deformation field of hyperelastic medium in equilibrium state is formulated into a constrained optimization problem under visual observation. By constructing finite element spatial discretization and visual observation point constraints, an efficient numerical algorithm is proposed for real-time deformation reconstruction of the soft contact medium. To validate the effectiveness of the proposed perceptual mechanisms, this paper optimizes the hardware structure and integrates the visuotactile algorithm for a class of flexible fingers with omnidirectional adaptive capability, endowing passive robotic flexible fingers with active proprioceptive shape sensing and contact force distribution sensing capabilities. Simulation and experimental results demonstrate the real-time performance (<= 50 ms) and accuracy (<= 2.5 mm) of the proposed sensing method in reconstructing contact deformations of flexible fingers. The deformation state of the soft robotic fingers during contact is encoded in the form of visual image. However, it is extremely challenging to decode the three-dimensional deformation field of flexible structures from two-dimensional images to obtain tactile information. For complex cross-medium visual-tactile perception tasks, this paper proposes a supervised variational autoencoder learning framework to establish a visual-tactile cross-modal perception model. The deformation information of flexible fingers encoded by visual images and mechanical principles are jointly encoded and represented as interpretable latent variable features. These latent variable features are used to convert between visual and tactile modalities, resulting in a more generalizable visuotactile sensing mechanism. Experimental studies are carried out on datasets and amphibious environments, confirming the effectiveness and accuracy of the cross-medium inference model for vision-based tactile perception (R2 >= 0.98). The application research of visuotactile sensing information in robotic dexterous manipulation mainly includes adaptive grasping and environment exploration. However, applying visuotactile sensing technology in amphibious environments and studying robotic dexterous manipulation based on flexible visuotactile sensing is extremely rare. This paper studies the grasping performance of the flexible finger visuotactile sensing system in amphibious environments, establishing robotic adaptive grasping experimental platforms onland and underwater to verify the adaptability and superiority of the proposed sensing system in amphibious grasping operations. Additionally, for the study of the environment exploration sensing performance of the flexible finger visuotactile sensing system, experiments were completed on two-dimensional weld seam tracking in industrial scenarios and three-dimensional object shape sensing in underwater salvage scenarios, validating the accuracy and robustness of the proposed sensing system in amphibious environment exploration tasks. The proposed perceptual mechanisms of vision-based soft tactile sensing through deformation reconstruction provides theoretical guidance and design basis for the design of new visuotactile sensor hardware structures and sensing algorithms. The application of robotic dexterous manipulation in amphibious environments opens new research directions and application fields for visuotactile sensing technology.
2.
Linhan Yang
Rigid-Soft Interactive Learning for Robotic Manipulation PhD Thesis
Southern University of Science and Technology & The University of Hong Kong, 2024.
@phdthesis{Yang2024PhDThesis,
title = {Rigid-Soft Interactive Learning for Robotic Manipulation},
author = {Linhan Yang},
year = {2024},
date = {2024-07-30},
urldate = {2024-07-30},
school = {Southern University of Science and Technology & The University of Hong Kong},
abstract = {Recent years have witnessed significant advancements in the field of robotic manipulation through the adoption of machine learning methods. Unlike other domains such as computer vision and natural language processing, robotic manipulation involves complex physical interactions that pose substantial challenges for developing scalable and generalized control policies. In this thesis, we explore the understanding and represnetation learning of these interactions across various robotic manipulation scenarios. We classify these interactions into two categories: Internal Interactions between the manipulator (gripper or robot) and the objects, and External Interactions involving the objects/robots and their external environments. Focusing on the internal interactions, we initially investigate a grasp prediction task. We change the variables such as gripper stiffness (rigid or soft fingers) and the type of grasp (power or precision), which implicitly encodes interaction data within our dataset. Our experiments reveal that this configuration greatly improves the training speed and the grasping performance. Furthermore, these interactions can be explicitly represented through force and torque data, facilitated by equipping the finger surfaces with multi-channel optical fibers. We have developed an interactive grasp policy that utilizes local interaction data. The proprioceptive capabilities of the fingers enable them to conform to object contact regions, ensuring a stable grasp. We then extend our research to include dexterous in-hand manipulation, specifically rotating two spheres within the hand by 180 degrees. During this task, interactions between the objects and the hand are continuously disrupted and reformed. We utilize a hand equipped with four fingers and a tactile sensor array to gather comprehensive interaction data. To effectively represent this data, we introduce the TacGNN, a generalized model for tactile information across various shapes. This model allows us to achieve in-hand manipulation using solely proprioceptive tactile sensing. In our exploration of external interactions between objects/robots and external environments, we begin with a rigid-rigid interaction within a loco-manipulation problem. Our aim is to merge interaction data from both locomotion and manipulation into a unified graph-based framework, encapsulated within the graph representation. A shared control policy is then developed through simulations and directly transferred to real-world applications in a zero-shot manner. Additionally, we investigate rigid-soft interactions through a fabric manipulation task involving deformable objects. We have developed a graph-based, environment-aware representation for fabric, which integrates environmental data. This model logically encodes interaction data, enabling each fabric segment to detect and respond to environmental contact. Employing this strategy, we successfully execute a goal-conditioned manipulation task: placing the fabric in a specified configuration within complex scenarios on the first attempt.},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
Recent years have witnessed significant advancements in the field of robotic manipulation through the adoption of machine learning methods. Unlike other domains such as computer vision and natural language processing, robotic manipulation involves complex physical interactions that pose substantial challenges for developing scalable and generalized control policies. In this thesis, we explore the understanding and represnetation learning of these interactions across various robotic manipulation scenarios. We classify these interactions into two categories: Internal Interactions between the manipulator (gripper or robot) and the objects, and External Interactions involving the objects/robots and their external environments. Focusing on the internal interactions, we initially investigate a grasp prediction task. We change the variables such as gripper stiffness (rigid or soft fingers) and the type of grasp (power or precision), which implicitly encodes interaction data within our dataset. Our experiments reveal that this configuration greatly improves the training speed and the grasping performance. Furthermore, these interactions can be explicitly represented through force and torque data, facilitated by equipping the finger surfaces with multi-channel optical fibers. We have developed an interactive grasp policy that utilizes local interaction data. The proprioceptive capabilities of the fingers enable them to conform to object contact regions, ensuring a stable grasp. We then extend our research to include dexterous in-hand manipulation, specifically rotating two spheres within the hand by 180 degrees. During this task, interactions between the objects and the hand are continuously disrupted and reformed. We utilize a hand equipped with four fingers and a tactile sensor array to gather comprehensive interaction data. To effectively represent this data, we introduce the TacGNN, a generalized model for tactile information across various shapes. This model allows us to achieve in-hand manipulation using solely proprioceptive tactile sensing. In our exploration of external interactions between objects/robots and external environments, we begin with a rigid-rigid interaction within a loco-manipulation problem. Our aim is to merge interaction data from both locomotion and manipulation into a unified graph-based framework, encapsulated within the graph representation. A shared control policy is then developed through simulations and directly transferred to real-world applications in a zero-shot manner. Additionally, we investigate rigid-soft interactions through a fabric manipulation task involving deformable objects. We have developed a graph-based, environment-aware representation for fabric, which integrates environmental data. This model logically encodes interaction data, enabling each fabric segment to detect and respond to environmental contact. Employing this strategy, we successfully execute a goal-conditioned manipulation task: placing the fabric in a specified configuration within complex scenarios on the first attempt.
1.
Chaoyang Song
Kinematic Study of Overconstrained Linkages and Design of Reconfigurable Mechanisms PhD Thesis
Nanyang Technological University, 2013.
@phdthesis{Song2013KinematicStudy,
title = {Kinematic Study of Overconstrained Linkages and Design of Reconfigurable Mechanisms},
author = {Chaoyang Song},
url = {https://hdl.handle.net/10356/55261},
year = {2013},
date = {2013-02-14},
urldate = {2013-02-14},
address = {Singapore},
school = {Nanyang Technological University},
abstract = {This dissertation explores the possibilities to design reconfigurable mechanisms using the kinematic and geometric properties of existing overconstrained linkages with revolute joints. Despite the large number of overconstrained linkages reported in literatures, there lacks of a comprehensive study into the relationship among them, which limits the understanding of the overconstrained linkages and their potential applications. The first part of this dissertation has been devoted to the systematic generalization of a series of double-Goldberg linkage families, in which the relationship between a number of existing linkages and their variational cases has been revealed. The common link-pair and common Bennett-linkage methods have been proposed to connect a Goldberg 5R linkage and a subtractive Goldberg 5R linkage to form six types of overconstrained linkage closures. Three sub-families, Wohlhart’s double-Goldberg linkages, mixed double-Goldberg linkages and double-subtractive-Goldberg linkages, have been generalized to represent the original cases, variational cases and subtractive cases of double-Goldberg linkage family. A substantial source of design for reconfigurable mechanisms in the Bennett-based linkage family has been presented in this part. In the second part, the kinematic study has been focused on the general line-symmetric Bricard linkage. The closure equations of the original and revised general line-symmetric Bricard linkages have been derived in explicit forms. For the general line-symmetric Bricard linkage, two independent and distinct linkage closures have been discovered. It has also been revealed that the revised cases are equivalent to the original cases with different setups on joint-axis directions. The potential of designing the reconfigurable mechanism through kinematic singularity has been demonstrated with the bifurcation behavior of the special line-symmetric Bricard linkage with zero offsets. The conceptual designs of reconfigurable mechanisms based on overconstrained linkages have been explored in the final part. Both the analytical and construct method have been presented to design morphing structures using overconstrained linkages. Based on the double-Goldberg linkage and the general line-symmetric Bricard linkage, reconfigurable mechanisms have been designed with multiple operation forms between 6R and 4R linkages. Furthermore, a generic method of link-pair replacement has been developed for reconfiguration purpose, which has been applied to reconfigure the topology of different Bennett linkage networks in order to obtain different overconstrained mechanisms. Results in this dissertation could lead to the substantial advancement in the design of reconfigurable mechanism with kinematic singularities. In the future work, the methods could be applied to design advanced reconfigurable robotic platforms with less actuators but more structural support.},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
This dissertation explores the possibilities to design reconfigurable mechanisms using the kinematic and geometric properties of existing overconstrained linkages with revolute joints. Despite the large number of overconstrained linkages reported in literatures, there lacks of a comprehensive study into the relationship among them, which limits the understanding of the overconstrained linkages and their potential applications. The first part of this dissertation has been devoted to the systematic generalization of a series of double-Goldberg linkage families, in which the relationship between a number of existing linkages and their variational cases has been revealed. The common link-pair and common Bennett-linkage methods have been proposed to connect a Goldberg 5R linkage and a subtractive Goldberg 5R linkage to form six types of overconstrained linkage closures. Three sub-families, Wohlhart’s double-Goldberg linkages, mixed double-Goldberg linkages and double-subtractive-Goldberg linkages, have been generalized to represent the original cases, variational cases and subtractive cases of double-Goldberg linkage family. A substantial source of design for reconfigurable mechanisms in the Bennett-based linkage family has been presented in this part. In the second part, the kinematic study has been focused on the general line-symmetric Bricard linkage. The closure equations of the original and revised general line-symmetric Bricard linkages have been derived in explicit forms. For the general line-symmetric Bricard linkage, two independent and distinct linkage closures have been discovered. It has also been revealed that the revised cases are equivalent to the original cases with different setups on joint-axis directions. The potential of designing the reconfigurable mechanism through kinematic singularity has been demonstrated with the bifurcation behavior of the special line-symmetric Bricard linkage with zero offsets. The conceptual designs of reconfigurable mechanisms based on overconstrained linkages have been explored in the final part. Both the analytical and construct method have been presented to design morphing structures using overconstrained linkages. Based on the double-Goldberg linkage and the general line-symmetric Bricard linkage, reconfigurable mechanisms have been designed with multiple operation forms between 6R and 4R linkages. Furthermore, a generic method of link-pair replacement has been developed for reconfiguration purpose, which has been applied to reconfigure the topology of different Bennett linkage networks in order to obtain different overconstrained mechanisms. Results in this dissertation could lead to the substantial advancement in the design of reconfigurable mechanism with kinematic singularities. In the future work, the methods could be applied to design advanced reconfigurable robotic platforms with less actuators but more structural support.
