@online{Wan2024SeeThruFinger,

title = {Multi-Layered Reasoning from a Single Viewpoint for Learning See-Through Grasping},

author = {Fang Wan and Chaoyang Song},

url = {https://doi.org/10.48550/arXiv.2312.09822

https://github.com/ancorasir/SeeThruFinger},

year  = {2026},

date = {2026-01-22},

abstract = {Sensory substitution enables biological systems to perceive stimuli that are typically perceived by another organ, which is inspirational for physical agents. Multimodal perception of intrinsic and extrinsic interactions is critical in building an intelligent robot that learns. This study presents a Vision-based See-Through Perception (VBSeeThruP) architecture that simultaneously perceives multiple intrinsic and extrinsic modalities from a single visual input, in a markerless manner, all packed into a soft robotic finger using the Soft Polyhedral Network design. It is generally applicable to miniature vision systems placed beneath deformable networks with a see-through design, capturing real-time images of the network's physical interactions induced by contact-based events, overlaid on the visual scene of the external environment, as demonstrated in the ablation study. We present the VBSeeThruP's capability for learning 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 multimodal performance of the VBSeeThruP architecture to simultaneously achieve various perceptions, including but not limited to scene inpainting, object detection, depth sensing, scene segmentation, masked deformation tracking, 6D force/torque sensing, and contact event detection, all within a single sensory input from the in-finger vision markerlessly.},

note = {Submitted to WAFR2026},

keywords = {Authorship - Corresponding, Status - Under Review},

pubstate = {forthcoming},

tppubtype = {online}

}

@online{Zhang2026WearableDilemma,

title = {The Wearable Dilemma in Life-Critical Hazardous Missions},

author = {Tuo Zhang and Wanghongjie Qiu and Fang Wan and Chaoyang Song},

year  = {2026},

date = {2026-01-15},

abstract = {Life-critical hazardous missions, such as those in space exploration, deep-sea operations, pandemic response, and nuclear emergencies, depend on the unique cognitive and adaptive capabilities of human operators. However, these individuals face a significant "wearable dilemma," where the protective systems essential for survival invariably compromise their mobility, endurance, and situational awareness, creating a fundamental trade-off between safety and performance. This paper analyzes this dilemma by characterizing life-critical missions and examining mission archetypes to identify the limitations of current wearable protective systems. Through case studies of spacesuits, atmospheric diving suits, hazmat suits, and personal protective equipment, the manuscript systematically evaluates inherent trade-offs such as protection versus mobility, weight versus endurance, complexity versus reliability, and isolation versus situational awareness. To resolve these challenges, the paper posits that emerging wearable robotics, including powered exoskeletons and supernumerary robotic limbs, offer a promising solution to augment human strength and endurance. Building on this, the manuscript introduces the conceptual framework of the SuperSuit, a novel paradigm for a next-generation intelligent wearable system that seamlessly integrates protective gear with robotic technologies. The SuperSuit is envisioned to enhance the operator's physical functions and mission execution capacity through intuitive, user-centric interfaces and shared control strategies, ultimately aiming to transform protective gear into an intelligent extension of the human body that amplifies unique human skills while mitigating operational risks.},

note = {Submitted to Wearable Technologies},

keywords = {Authorship - Corresponding, Status - Under Review},

pubstate = {forthcoming},

tppubtype = {online}

}

@online{Tian2026ObjectRetrieval,

title = {Object Retrieval in Cluttered Scenes using Two-Stage Graph Neural Networks},

author = {Hao Tian and Ruozhou Wang and Feilong Zhou and Chaoyang Song and Jia Pan and Weihua Ou},

year  = {2025},

date = {2025-11-24},

abstract = {In this study, we propose a novel object retrieval framework in

cluttered environments based on a two-stage Graph Neural Network (GNN)

architecture. The proposed method leverages a Traversability Graph

(T-graph) to represent the spatial layout of objects and their movable

relationships, enabling efficient planning for retrieving a target

object obstructed by multiple obstacles. In the first stage, an Edge

Prediction Network (EdgeNet) predicts feasible connections between

object pairs to construct the T-graph from spatial distributions. In

the second stage, a Path Prediction Network (PathNet) estimates the

optimal retrieval sequence on the predicted T-graph, indicating the

order of object removals required to access the target. To address

execution failures caused by collisions or kinematic constraints during

manipulation, we integrate the learned models with a heuristic Lifelong

Planning A* (LPA*) algorithm, facilitating efficient online replanning.

Extensive simulation and physical robot experiments demonstrate that

our approach significantly improves retrieval efficiency and success

rate compared with existing graph-based and search-based methods,

particularly in highly cluttered scenes with numerous objects. The

results verify that the proposed two-stage GNN framework provides a

scalable and data-driven solution for robotic object retrieval in

complex, confined environments.},

note = {Submitted to IEEE Robotics and Automation Letters},

keywords = {Authorship - Co-Author, Status - Under Review},

pubstate = {forthcoming},

tppubtype = {online}

}

@online{Zhong2025TopologyOptimized,

title = {Topology-Optimized, Dual-Phase Soft Gripper with Embedded Force Estimation for Damage-Aware Underwater Operation},

author = {Shuqiao Zhong and He Zheng and Yihong Yao and Fang Wan and Zhiyuan Zhou and Chaoyang Song and Jian Lin},

year  = {2025},

date = {2025-11-15},

abstract = {Addressing the critical trade-off between compliance and grasping force in underwater manipulation, this paper presents a novel topology optimization framework to automate the design of soft, variable-stiffness fingers from a single material. By employing multiple load cases within the optimization objective, our framework automatically synthesizes a finger structure that preserves the adaptive Fin Ray effect while exhibiting programmed, multi-stage stiffness. We utilize this method to realize a dual-phase finger, characterized by low initial stiffness for compliant object contact and high subsequent stiffness for secure grasping. Quantitative comparisons validate this achievement: the optimized design yields 351% of the grasping force of a Fin Ray finger with comparable softness, while simultaneously offering 286% of the adaptation of a high-force Fin Ray variant. To enable damage-aware teleoperation, a flex sensor is embedded within the finger structure. We establish a mapping to grasping force via a piecewise-regressed model, whose structure directly reflects the finger's dual-phase mechanical behavior. The complete gripper is demonstrated as a modular end-effector on a commercial underwater arm, successfully performing damage-free grasping of diverse objects, from compliant sea cucumbers to rigid stones. This work establishes a new pathway for designing automated, single-material soft grippers with direct applications in complex underwater environments.},

note = {Submitted to IEEE Robotics and Automation Letters},

keywords = {Authorship - Corresponding, Status - Under Review},

pubstate = {forthcoming},

tppubtype = {online}

}

@online{Sun2025OverconstrainedLocomotion,

title = {Overconstrained Locomotion},

author = {Haoran Sun and Bangchao Huang and Zishang Zhang and Junwei Lv and Guangyi Huang and Jiayi Yin and Shihao Feng and Ronghan Xu and Guojing Huang and Nuofan Qiu and Hua Chen and Wei Zhang and Fang Wan and Jia Pan and Chaoyang Song},

url = {https://github.com/ancorasir/OverconstrainedLocomotion},

year  = {2025},

date = {2025-10-01},

abstract = {This paper presents a foundational study on the design, modeling, and control of a novel robotic limb that generates overconstrained locomotion. By utilizing a Bennett linkage for motion generation, the limb enables parametric reconfiguration between reptile- and mammal-inspired morphologies within a single quadrupedal platform. While the family of overconstrained linkages has solid theoretical foundations in spatial kinematics, it remains underexplored in dynamic robotics. This work establishes the morphological and performance advantages of Overconstrained Robotic Limbs (ORLs), which can transform into prevailing planar limbs. We develop a complete kinematic and dynamic model of the ORL and apply Model Predictive Control (MPC) to validate its performance. Hardware experiments on a single-limb prototype validate the core mechanical design and actuation principles. Furthermore, extensive high-fidelity simulations of the complete quadruped confirm the efficacy of our control framework for a range of dynamic locomotion tasks over challenging terrains. Besides the clear advantage in lateral mobility, our comparative analysis reveals the ORL's superior energy efficiency over planar limbs when considering varying foothold distances and speeds. From an evolutionary biology perspective, these findings provide a quantitative insight into the distinctive biomechanical trade-offs in limb design, presenting the first comprehensive analysis of how ORLs outperform planar designs in simulated dynamic locomotion.},

note = {Submitted to the Special Collection for ISRR24 in the International Journal of Robotics Research},

keywords = {Authorship - Corresponding, Status - Under Review},

pubstate = {forthcoming},

tppubtype = {online}

}

@online{Liu2025ABiomimetic,

title = {A Biomimetic Vertebraic Soft Robotic Tail for High-Speed, High-Force Dynamic Maneuvering},

author = {Sicong Liu and Jianhui Liu and Fang Chen and Wenjian Yang and Juan Yi and Yu Zheng and Zheng Wang and Wanchao Chi and Chaoyang Song},

doi = {10.48550/arXiv.2509.20219},

year  = {2025},

date = {2025-09-23},

abstract = {Robotic tails can enhance the stability and maneuverability of mobile robots, but current designs face a trade-off between the power of rigid systems and the safety of soft ones. Rigid tails generate large inertial effects but pose risks in unstructured environments, while soft tails lack sufficient speed and force. We present a Biomimetic Vertebraic Soft Robotic (BVSR) tail that resolves this challenge through a compliant pneumatic body reinforced by a passively jointed vertebral column inspired by musculoskeletal structures. This hybrid design decouples load-bearing and actuation, enabling high-pressure actuation (up to 6 bar) for superior dynamics while preserving compliance. A dedicated kinematic and dynamic model incorporating vertebral constraints is developed and validated experimentally. The BVSR tail achieves angular velocities above 670°/s and generates inertial forces and torques up to 5.58 N and 1.21 Nm, indicating over 200% improvement compared to non-vertebraic designs. Demonstrations on rapid cart stabilization, obstacle negotiation, high-speed steering, and quadruped integration confirm its versatility and practical utility for agile robotic platforms.},

note = {Submitted to IEEE Transactions on Robotics},

keywords = {Authorship - Corresponding, Status - Under Review},

pubstate = {forthcoming},

tppubtype = {online}

}