论文概要
研究领域: RO 作者: Yunhai Feng, Natalie Leung, Jiaxuan Wang, Lujie Yang, Haozhi Qi 发布时间: 2026-07-13 arXiv: 2607.11874
中文摘要
近期人形全身控制研究采用了一种简单有效的方案：将人体动作重定向到机器人运动学参考，然后通过强化学习训练策略跟踪这些参考。但这一方案如何迁移到灵巧操作？答案并不显然，因为操作涉及复杂的接触丰富动力学，需要精细调节接触模式和力。本文提出 REGRIND，一种极简的重定向引导RL流程，从单个人类演示中学习灵巧操作策略。REGRIND将人手-物体运动重定向到保留手-物体空间与接触关系的机器人参考，在仿真中训练残差RL策略跟踪该参考的物体中心关键点，然后通过精心的系统识别将结果策略零样本迁移到硬件。所得策略在两种不同多指手上产生流畅、类人行为，完成包括操作剪刀和转动螺丝刀等接触丰富的工具使用任务。通过系统硬件实验，识别并分析了灵巧操作中仿真到真实迁移的关键影响因素，为接触丰富场景中的重定向学习提供了实用指导。
原文摘要
Recent work in humanoid whole-body control has found success with a simple recipe: retarget human motion to robot kinematic references, then train policies via reinforcement learning (RL) to track them. But how does this recipe transfer to dexterous manipulation? The answer is not obvious, as manipulation involves complex, contact-rich dynamics and requires delicate regulation of contact modes and forces. We present REGRIND, a minimalist retargeting-guided RL pipeline that learns dexterous manipulation policies from a single human demonstration. REGRIND retargets human hand-object motion to a robot reference that preserves hand-object spatial and contact relationships, trains a residual RL policy in simulation to track object-centric keypoints along that reference, and transfers the result…
— 自动采集于 2026-07-15
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