全身灵巧操作（WBD）：让 Unitree G1 用躯干和手臂夹持搬运物体

View PDF HTML (experimental)

Abstract:Current humanoid dexterous manipulation remains largely confined to end-effectors and simple grasps, falling short of humans' ability to engage the entire body by bracing and sliding bulky objects against the arms and torso. We introduce Whole-Body Dexterity (WBD), a general framework that uses the robot's body and appendages as contact surfaces to grasp and manipulate objects. Our sim-to-real pipeline first generates a dataset of feasible whole-body grasps, then trains reinforcement-learning experts on a general any-to-any repositioning objective by sampling initial and goal grasps from the dataset, and distills these experts into a single policy that uses proprioception and object geometry. We show that the distilled policy transfers zero-shot to a real Unitree G1, where it repositions a basketball and a cardboard box to regions around the torso and completes a long-horizon task that stows a box under one arm, carries it from a shelf to a table, and places it. The controller operates interactively, steered by coarse motion references from a human teleoperator. Across real-world whole-body manipulation tasks, the distilled controller completes 90% of trials, compared with 18% for direct retargeting and 4% for a generalist whole-body controller.

Comments:	20 pages, 13 figures
Subjects:	Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Systems and Control (eess.SY)
Cite as:	arXiv:2610.22184 [cs.RO]
	(or arXiv:2610.22184v1 [cs.RO] for this version)
	https://doi.org/10.48550/arXiv.2610.22184 arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Conor Mc Gartoll [view email]
[v1] Thu, 15 Oct 2026 16:40:48 UTC (27,224 KB)