接触感知的模仿学习：通过接触因子分解

中文摘要 AI 辅助

可泛化的接触丰富型操作要求机器人在适应不断变化的接触条件的同时，保持预期的任务行为。然而，交互力会随着表面几何形状、方向和摩擦力的微小变化而显著变化，这使得直接在原始力测量上训练的策略难以迁移到演示条件之外。我们提出了FACE，一个接触因子分解的模仿学习框架，它将预期的任务行为与环境相关的接触因子分离开来。我们的表示将交互力表达在归一化的、接触相对坐标中，而一个学习的接触法线估计器和一个在线摩擦力估计器推断局部接触法线和有效摩擦尺度。这些估计器共同使得力观测能够被编码，策略输出能够在执行过程中被解码为物理运动和力命令。通过这种方式，FACE在不更新策略参数的情况下，适应当前的接触条件，同时保持预期的任务行为。我们在真实机器人上，在表面属性和几何形状的未见变化下评估了FACE，通过与适应先前方法到我们设置的变体进行受控比较，展示了在接触条件下的稳健泛化。视频和其他材料可在项目页面找到：此https URL。

英文摘要

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.

发表机构

• Kyung Hee University（庆熙大学）
• Ewha Womans University（梨花女子大学）
• University of Central Florida（中佛罗里达大学）

机构由 AI 辅助整理，请以论文原文为准。