CHIP：通过回顾扰动实现人形机器人适应性合规性控制
CHIP: Adaptive Compliance for Humanoid Control through Hindsight Perturbation
AI总结：
CHIP通过回顾扰动实现人形机器人可控的末端执行器刚度，无需额外调优即可完成多种力量操作任务。
摘要：
近年来，人形机器人的进展解锁了敏捷运动技能，包括后翻、跑步和爬行。然而，对于人形机器人执行需要力量操作的任务，如移动物体、擦拭和推车仍然具有挑战性。我们提出了通过回顾扰动实现适应性合规人形控制（CHIP），这是一个即插即用模块，能够在保持动态参考运动敏捷跟踪的同时，实现可控的末端执行器刚度。CHIP易于实现，无需数据增强或额外奖励调优。我们展示了一种通过CHIP训练的通用运动跟踪控制器，能够执行一组需要不同末端执行器合规性的力量操作任务，如多机器人协作、擦拭、箱子递送和开门。
英文摘要：
Recent progress in humanoid robots has unlocked agile locomotion skills, including backflipping, running, and crawling. Yet it remains challenging for a humanoid robot to perform forceful manipulation tasks such as moving objects, wiping, and pushing a cart. We propose adaptive Compliance Humanoid control through hIsight Perturbation (CHIP), a plug-and-play module that enables controllable end-effector stiffness while preserving agile tracking of dynamic reference motions. CHIP is easy to implement and requires neither data augmentation nor additional reward tuning. We show that a generalist motion-tracking controller trained with CHIP can perform a diverse set of forceful manipulation tasks that require different end-effector compliance, such as multi-robot collaboration, wiping, box delivery, and door opening.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
