接触因子分解による接触を考慮した模倣学習
Contact-Aware Imitation Learning Through Contact Factorization
接触力を正規化された接触相対座標で表現し、接触法線と摩擦を推定することで、未知の接触条件でも方策を更新せずに接触の多い操作を適応させる模倣学習フレームワークFACEを提案。
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著者: Jiho Hong, Daeun Song, Sanghyun Kim, Mingyo Seo
分類: cs.RO
原文アブストラクト
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.