Working Papers
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Journal Articles
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Conference Papers
7.
Xudong Han, Ning Guo, Shuqiao Zhong, Zhiyuan Zhou, Jian Lin, Chaoyang Song, Fang Wan
Proprioceptive State Estimation for Amphibious Tactile Sensing Conference
IEEE International Conference on Robotics and Automation (ICRA2025), Atlanta, USA, 2025, (Dual-track Submission with TRO: https://doi.org/10.1109/TRO.2024.3463509).
Abstract | Links | BibTeX | Tags: Authorship - Corresponding, Conf - ICRA, Special - Dual-Track
@conference{Han2025ProprioceptiveState,
title = {Proprioceptive State Estimation for Amphibious Tactile Sensing},
author = {Xudong Han and Ning Guo and Shuqiao Zhong and Zhiyuan Zhou and Jian Lin and Chaoyang Song and Fang Wan},
url = {https://github.com/ancorasir/PropSE},
doi = {10.1109/TRO.2024.3463509},
year = {2025},
date = {2025-03-07},
urldate = {2025-03-07},
booktitle = {IEEE International Conference on Robotics and Automation (ICRA2025)},
address = {Atlanta, USA},
abstract = {This paper 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 omni-directional 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 accuracies, 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. All codes are shared on GitHub: https://github.com/ancorasir/PropSE.},
note = {Dual-track Submission with TRO: https://doi.org/10.1109/TRO.2024.3463509},
keywords = {Authorship - Corresponding, Conf - ICRA, Special - Dual-Track},
pubstate = {published},
tppubtype = {conference}
}
This paper 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 omni-directional 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 accuracies, 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. All codes are shared on GitHub: https://github.com/ancorasir/PropSE.
6.
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.
5.
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.
4.
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.
3.
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.
2.
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}
}
1.
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}
}
Extended Abstracts
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
Sorry, no publications matched your criteria.