外观
论文arXiv 2610.0953321 天前
通过接触因子分解的接触感知模仿学习
原文链接：Contact-Aware Imitation Learning Through Contact Factorization（arXiv 2610.09533 · 发布日期：2026-10-07）
Contact-Aware Imitation Learning Through Contact Factorization
FACE将力分解为接触相对表示并估计法向与摩擦，在真实机器人接触丰富操作中验证跨接触条件泛化。
核心要点
- 接触丰富操作需保持任务意图，同时适应表面几何、姿态和摩擦变化。
- FACE用归一化接触相对坐标表示力，分离任务行为与环境接触因素。
- 学习接触法向估计器和在线摩擦估计器，执行时编码观测并解码命令。
- 真实机器人实验覆盖未见表面属性和几何变化，并与适配既有方法的变体对比；提供视频和材料。
验证范围与局限：验证范围为真实机器人接触丰富操作，覆盖未见表面属性和几何变化。
英文摘要 (Abstract)
Generalizable contact-rich manipulation requires robots to preserve intended task behavior while adapting its physical realization to changing contact conditions. However, interaction forces can vary substantially with small changes in surface geometry, orientation, and friction, making policies trained directly on raw force measurements difficult to transfer beyond demonstrated conditions. We introduce FACE, a contact-factorized imitation learning framework that separates intended task behavior from environment-dependent contact factors. Our representation expresses interaction forces in normalized, contact-relative coordinates, while a learned contact-normal estimator and an online friction estimator infer the local contact normal and effective friction scale. Together, these estimators enable force observations to be encoded and policy outputs to be decoded into physical motion and force commands during execution. In this way, FACE adapts execution to current contact conditions while preserving the intended task behavior, without updating the policy parameters. We evaluate FACE on real-robot contact-rich manipulation under unseen variations in surface properties and geometry, demonstrating robust generalization across contact conditions through controlled comparisons with variants that adapt prior approaches to our setting. Videos and additional materials can be found on the project page: https://rcilab.khu.ac.kr/face.
作者：Jiho HOng, Daeun Song, Sanghyun Kim, Mingyo Seo
来源
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