论文/arXiv 2609.24093·1 个月前

通过接触锚定重定向和残差策略学习从人类示教中进行灵巧机器人操作

针对人类示教缺少接触力的数据瓶颈，提出接触锚定重定向与残差策略学习，并在仿真重建、机器人手迁移和真机双手任务上验证。

AI 整理

• 硬件零部件
• 本体 SDK 与控制
• 数据与采集

原文链接：Dexterous Robot Manipulation from Human Demonstrations via Contact-Anchored Retargeting and Residual Policy Learning（arXiv 2609.24093 · 发布日期：2026-09-21）

Dexterous Robot Manipulation from Human Demonstrations via Contact-Anchored Retargeting and Residual Policy Learning

针对人类示教缺少接触力的数据瓶颈，提出接触锚定重定向与残差策略学习，并在仿真重建、机器人手迁移和真机双手任务上验证。

核心要点

• 人类示教的可扩展来源缺少决定抓取成败的接触力，限制灵巧操作学习。
• 方法保留手指区域与物体位置的接触结构，用接触锚定目标跨手型重定向。
• 共享残差RL策略先修复运动捕捉为物理一致轨迹，再适配机器人驱动。
• 单手成功率7.3%升至59.3%，双手16.0%升至62.4%，迁移提升62.4个百分点。

验证范围与局限：验证范围包括25,454条单手轨迹、25个双手任务、四种形态不同的机器人手，以及物理硬件上的四个接触丰富双手任务。

英文摘要 (Abstract)

Learning dexterous manipulation from demonstrations is bottlenecked by data: the contact forces that determine whether a grasp succeeds are absent from every scalable source of human demonstrations. This paper builds on two observations. First, what survives the change from a human hand to a robot hand is the contact structure of a demonstration - which finger regions touch which object locations, and in what order - rather than its joint motion. Second, physical consistency need not be engineered per task: a single residual reinforcement learning (RL) policy, trained once across diverse demonstrations, can repair kinematic recordings into physically consistent, contact-annotated trajectories, and the same residual formulation restores dynamic feasibility after retargeting. These observations yield a three-stage pipeline that converts human motion-capture recordings into dexterous robot policies with no real-robot training data: physics refinement with a simulated MANO hand recovers contacts and forces, contact-anchored retargeting transfers the demonstrated contact structure through an objective independent of hand morphology, and residual policy learning adapts the result to robot actuation. The pipeline reconstructs 25,454 single-hand trajectories (success 7.3% -> 59.3%) and 25 dual-hand tasks (16.0% -> 62.4%) with one shared policy per setting, transfers one human dataset to four morphologically distinct robot hands (+62.4 pp), and executes four contact-rich bimanual tasks on physical hardware with zero real-robot training data.

作者：Zihao Yang, Chengyuan Liu, Yu Zhou, Runze Lv, Tianyu Cui, Sheng Yi, Haohua Zhu, Irvine Lu, JieQ Sun

来源

本页事实对应的公开页面，以原文为准。

1. arXiv 2609.24093：Dexterous Robot Manipulation from Human Demonstrations via Contact-Anchored Retargeting and Residual Policy Learning访问 2026-10-21