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学习力量——反应型人形操控装置

原文标题 · Learning Force-Reactive Humanoid Loco-Manipulation

作者:Sihang Li, Xiaofeng Guo, Jinkun Cao 许多日常任务都需要控制接触力以及物体的运动。对于仿人机器人来说,这种力通常需要通过双手和整个身体的协作来施加,而无法仅通过运动轨迹来精确控制。因此,我们提出了FLORE这一框架,用于学习如何使仿人机器人能够灵活地进行力的操控。我们收集了没有机器人的演示案例,这些案例使用了两种基于力感知技术的UMI式手持抓取装置。

来源:arXiv cs.AI/CL/LG · arxiv.org

正文为英文,可一键机器翻译(仅首次需要等待)

Computer Science > Robotics

Title: Learning Force-Reactive Humanoid Loco-Manipulation

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 .

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