arXiv AI
Sep 17

Learning Multi-Humanoid Pickup and Transport via Decentralized Object-Centric Control

The paper presents a decentralized, object‑centric control strategy for cooperative multi‑humanoid pickup and transport of objects with diverse sizes, weights, and shapes. Each humanoid is assigned a local attachment region on the shared object and learns to perform gripperless bimanual pinching, enabling pickup, transport, and handover without task‑specific redesign. Experiments in simulation and on real hardware demonstrate that single‑robot trained policies transfer to multi‑robot settings and that additional multi‑robot training further improves coordination.

By Bikram Pandit, Mohitvishnu S. Gadde, Aayam Kumar Shrestha, Alan Fern
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
arXiv AI
Aug 18

HAF: Adapting Generalist VLAs to Humanoid Whole-Body Loco-manipulation via Hierarchical Action Flow and Spectral Latent RL

arXiv:2608. 16837v1 Announce Type: cross Abstract: Humanoid robots hold great promise as general-purpose agents in human-centered environments, yet generalist vision-language-action (VLA) foundation models are not readily applicable to humanoid whole-body loco-manipulation.

By Langzhe Gu, Chengkai Hou, Meng Li, Xinhua Wang, Jiaming Liu, Xinyuan Lv, Bowei Zhang, Shuanghao Bai, Guangrun Li, Jingyang He, Gaole Dai, Ziluo Ding, Zhiyuan Xu, Kuan Cheng, Jian Tang, Zhengping Che, Shanghang Zhang