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.

翻译：人形机器人领域的最新进展已实现包括后空翻、奔跑和爬行在内的灵活动作技能。然而，人形机器人执行需要较大力的操作任务（如移动物体、擦拭和推车）仍面临挑战。本文提出基于后视扰动的自适应柔顺人形控制模块（CHIP），该即插即用模块可在保持对动态参考动作敏捷跟踪的同时，实现可控的末端执行器刚度。CHIP易于实现，既无需数据增强，也无需额外奖励函数调优。研究表明，结合CHIP训练的通用运动跟踪控制器能够执行多种需要不同末端执行器柔顺性的强力操作任务，例如多机器人协作、擦拭、箱体搬运和开门。