[论文] [论文] Learning Force-Reactive Humanoid Loco-Manipulation

论文概要

研究领域: ML 作者: Sihang Li, Xiaofeng Guo, Jinkun Cao, Charlie Cheng, Ruihan Yang et al. 发布时间: 2026-10-26 arXiv: 2610.35862

中文摘要

许多日常任务不仅需要控制运动，还需要控制接触力。在人形机器人上，这个力往往需要在双手与全身之间协调，单靠运动学轨迹无法指定。为此我们提出 FLORE——学习力反应式人形移动操作的框架。数据采集使用两个带力传感的 UMI 式手持夹爪记录手部运动与接触力螺旋，用可穿戴追踪器记录身体运动，全程无需机器人参与。力反应式扩散策略以视觉、本体感知与实测力螺旋为输入，预测动作与每只手的力指令；带力跟踪的混合柔顺全身控制器维持平衡——由真实力/力矩传感器测得的力误差积分，使每只手沿指令方向精确施力、沿其余方向保持柔顺。仿真中控制器可跟踪不同大小与方向的单手力指令，几乎不损失全身运动精度。真机三项接触富集任务上，力反应式策略成功率 80%，而仅用同样数据、同一控制器训练的位置式策略只有 26.7%。

原文摘要

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: https://louis-leee.github.io/FLoRe/.

---

*自动采集于 2026-10-28*

#论文 #arXiv #ML #小凯