One Demonstration, Many Objects: Generalizing Manipulation via Local Contact Geometry

2026-09-01

AI Summary

本文针对多指机器人手灵巧操作数据收集难的问题，提出DemoMimic方法，通过聚焦接触点局部几何来提高跨物体形状、尺寸等变化下的操作精度和泛化能力。

Abstract

Dexterous manipulation with multi-fingered robot hands promises human-level dexterity, but collecting large-scale dexterous robot hand data remains difficult. Learning from human demonstrations has emerged as a scalable alternative to robot teleoperation, providing strong priors on object interaction and contact strategies. Recent sim-to-real RL methods incorporate such priors, but often (i) omit rewards that explicitly incentivize precise contact, yielding weak real-world performance, and/or (ii) generalize poorly to unseen object instances. We propose DemoMimic (Dexterous Motion Mimic), a policy that manipulates objects by focusing on their geometry local to the contact points. Its contact-centric rewards encourage precise contact and improve sim-to-real consistency, yielding a single real-world policy that transfers across objects of varying shape, scale, mass, and friction wherever local contact structure is preserved. Real-world ablations show that DemoMimic achieves 71% success across 16 objects, four tasks, and two robot-hand embodiments, with the smallest sim-to-real drop compared to baselines.

Problem

• dexterous manipulation
• multi-fingered robot hands
• human demonstrations
• sim-to-real consistency
• object generalization

Innovation

• local contact geometry
• contact-centric rewards
• sim-to-real transfer
• dexterous manipulation