让机器人通过学习动态调整接触时的柔顺性，更好完成复杂操作任务。
Adaptive Compliance Policy: Learning Approximate Compliance for Diffusion Guided Control
- 从人类示范中学习时空可变的柔顺策略，替代固定参数
- 相比现有方法，任务成功率提升超50%
- 适合需要精准力控的复杂抓取与装配场景
柔顺控制在机器人操作中至关重要，能在不确定环境下平衡位置与力的协同控制。然而，当前视觉-运动策略普遍只关注位置控制，忽略柔顺性。本文提出自适应柔顺策略（ACP），通过人类示范学习任务相关的时空动态柔顺调节机制，改进以往依赖预设或恒定刚度参数的方法。由于直接从示范中推导完整柔顺参数是病态问题，我们转而估计一种近似柔顺轮廓，具备两个关键特性：避免产生过大接触力，同时促进轨迹精确跟踪。该方法使机器人能高效处理复杂的接触密集型操作任务，在性能上相较最先进视觉-运动策略提升超过50%。视频演示见 https://adaptive-compliance.github.io/
原文摘要 · Abstract (English)
Compliance plays a crucial role in manipulation, as it balances between the concurrent control of position and force under uncertainties. Yet compliance is often overlooked by today's visuomotor policies that solely focus on position control. This paper introduces Adaptive Compliance Policy (ACP), a novel framework that learns to dynamically adjust system compliance both spatially and temporally for given manipulation tasks from human demonstrations, improving upon previous approaches that rely on pre-selected compliance parameters or assume uniform constant stiffness. However, computing full compliance parameters from human demonstrations is an ill-defined problem. Instead, we estimate an approximate compliance profile with two useful properties: avoiding large contact forces and encouraging accurate tracking. Our approach enables robots to handle complex contact-rich manipulation tasks and achieves over 50\% performance improvement compared to state-of-the-art visuomotor policy methods. For result videos, see https://adaptive-compliance.github.io/
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