The paper introduces an adaptive stiffness control framework for physical human‑robot collaboration that uses generative action‑chunk sampling conditioned on RGB images and joint‑torque estimates. By sampling multiple future action chunks, the system measures variation among them to adjust joint stiffness and damping in real time—higher variation leads to more compliance, while lower variation yields firmer assistance. In a collaborative transport experiment with four possible directions, the method achieved a 0.95 success rate, outperforming a fixed‑stiffness baseline (0.83) and a deterministic baseline (0.69).
By Aoi Otake, Ferdinand Hartmann, Ko Igari, Shingo Murata
arXiv:2606. 14218v1 Announce Type: cross Abstract: For robots to work safely in household environments, they need to be compliant and react to torque and force feedback during contact.
By Litian Liang, Jingxi Xu, Xinda Qi, Yujun Cai, Houzhu Ding, Luqi Wang, Zhixin Sun, Jyh-Herng Chow, Ming Yang, Mark Cutkosky
arXiv:2608. 15995v1 Announce Type: cross Abstract: Upper extremity motor function recovery is positively linked to Task-Specific Training (TST) and sufficient therapy dosage.
By Jia Quan Loh (Human Robotics Laboratory, Department of Mechanical Engineering, The University of Melbourne), Vincent Crocher (Human Robotics Laboratory, Department of Mechanical Engineering, The University of Melbourne), Marlena Klaic (Melbourne School of Health Sciences, The University of Melbourne), Denny Oetomo (Human Robotics Laboratory, Department of Mechanical Engineering, The University of Melbourne), Ying Tan (Human Robotics Laboratory, Department of Mechanical Engineering, The University of Melbourne)
FWBC‑VLA is a force‑aware framework that links vision‑language‑action (VLA) models with whole‑body compensation control for wheeled‑legged robots. It introduces HSR‑Force, a sensorless residual‑torque estimator that infers contact strength and encodes this information as tokens for the VLA action decoder, allowing the policy to detect contact onset, loading, and release. The system fine‑tunes a pretrained VLA backbone on a large WL&Arm dataset, combines proprioceptive, Jacobian‑derived force, and contact estimates to generate corrective actions, and demonstrates effectiveness in real‑world tasks such as whiteboard wiping and door opening.
By Yutian Zhang, Siyuan Ma, Liwen Yang, Yang Li, Ce Hao, Haozhen Chi, Dong We, Qiaojun Yu, Dibo Hou
arXiv:2606. 12406v1 Announce Type: cross Abstract: Contact-rich manipulation requires force sensitivity, but many robot arms lack dedicated force sensors due to their high cost.
By Steven Oh, Jason Jingzhou Liu, Tony Tao, Philip Han, Kenneth Shaw, Satoshi Funabashi, Ruslan Salakhutdinov, Deepak Pathak
arXiv:2507. 21638v2 Announce Type: replace Abstract: The development of reinforcement learning (RL) algorithms has been largely driven by ambitious challenge tasks and benchmarks.
By Leonard Hinckeldey, Elliot Fosong, Rimvydas Rubavicius, Elle Miller, Trevor McInroe, Fan Zhang, Patricia Wollstadt, Stefano V. Albrecht, Subramanian Ramamoorthy