← Back to all news
arXiv Machine Learning By Aoi Otake, Ferdinand Hartmann, Ko Igari, Shingo Murata

Generative Action-Chunk Sampling for Adaptive Stiffness Control in Physical Human-Robot Collaboration

Read the original on arXiv Machine Learning →

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).

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv AI
Jul 20

Interaction-Aware Whole-Body Control for Compliant Object Transport

arXiv:2603. 03751v2 Announce Type: replace-cross Abstract: Cooperative object transport in unstructured environments remains challenging for assistive humanoids because strong, time-varying interaction forces can make tracking-centric whole-body control unreliable, especially in close-contact support tasks.

By Hao Zhang, Yves Tseng, Ding Zhao, H. Eric Tseng
agentsreinforcement-learningroboticsefficiency
More like this →
arXiv Computer Vision
Sep 18

DexTouch-WM: Learning Action-Conditioned Tactile World Models from Human Touch for Dexterous Robot Manipulation

DexTouch-WM is an action‑conditioned world model that learns from scalable human touch to predict future RGB observations and bilateral tactile dynamics for dexterous robot manipulation. By using compatible piezoresistive arrays on both human and robot hands and retargeting human motion into the robot action space, the model can be supervised with human interaction data while keeping a fixed amount of real‑robot supervision. Experiments show that adding up to 100 hours of human interaction improves robot‑domain visual, geometric, and contact prediction, and the model can serve as a surrogate environment for policy evaluation and synthetic trajectory generation.

By Yan Qin, Yue Chen, Wenwei Lin, Shujia Liu, Chuqiao Lyu, Kailun Su, Chenze Yu, Ping Luo, Wenbo Ding, Tianxing Chen, Renjing Xu
robotics
More like this →