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/

翻译：柔顺性在机器人操作中起着至关重要的作用，它能在不确定性条件下平衡位置与力的协同控制。然而，当前专注于位置控制的视觉运动策略往往忽视了柔顺性。本文提出了自适应柔顺策略（ACP），这是一种新颖的框架，能够从人类演示中学习为给定操作任务在空间和时间上动态调整系统柔顺性，改进了以往依赖预选柔顺参数或假设均匀恒定刚度的方案。然而，从人类演示中计算完整的柔顺参数是一个不适定问题。为此，我们估计了一种具有两个有用特性的近似柔顺轮廓：避免大的接触力并鼓励精确跟踪。我们的方法使机器人能够处理复杂的富接触操作任务，与最先进的视觉运动策略方法相比，性能提升超过50%。结果视频请参见 https://adaptive-compliance.github.io/