FLORE：学习力反应式人形机器人移动操作框架

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Abstract:Many everyday tasks require controlling contact force as well as motion. On a humanoid, this force must often be coordinated across both hands and the whole body, and cannot be specified by kinematic trajectories alone. Therefore, we present FLORE, a framework for learning force-reactive humanoid loco-manipulation. We collect robot-free demonstrations using two force-sensing UMI-style handheld grippers for hand motion and contact wrenches, and wearable trackers for body motion. A force-reactive diffusion policy takes vision, proprioception, and measured wrenches as input, then predicts actions and per-hand force commands. A hybrid compliance whole-body controller with force tracking executes these commands while maintaining balance: an integral of the force error measured by real force/torque (F/T) sensors lets each hand track force accurately along the commanded direction and remain compliant along the others. In simulation, the controller tracks per-hand force commands of varied magnitude and direction, with little loss in whole-body motion accuracy. Across three contact-rich tasks on the robot, the force-reactive policy succeeds in 80% of rollouts, compared with 26.7% for a position-only policy trained on the same demonstrations and executed by the same controller. For more details, please visit our website: this https URL.

Comments:	9 pages, 6 figures
Subjects:	Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Systems and Control (eess.SY)
Cite as:	arXiv:2610.35862 [cs.RO]
	(or arXiv:2610.35862v1 [cs.RO] for this version)
	https://doi.org/10.48550/arXiv.2610.35862 arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Sihang Li [view email]
[v1] Mon, 26 Oct 2026 17:59:11 UTC (36,849 KB)