发表机构
Queensland University of Technology; Australian Cobotics Centre; Centre for Robotics（昆士兰科技大学; 澳大利亚协同机器人中心; 机器人中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出支撑手策略（SHS），通过任务条件化环境支撑，使单臂有力操作时另一臂支撑，显著提升人形机器人接触力至60N，远超无支撑的13.5N，且无需重新训练即可泛化。
AI 中文摘要
有力操作对人形机器人具有挑战性，因为交互力可能干扰全身平衡。我们引入了支撑手策略（SHS），该策略使人形机器人能够用一只手对环境进行支撑，同时用另一只手执行有力操作。SHS优化了一个任务条件化的支撑配置，该配置指导两个同步的强化学习策略，无需人类运动数据或在线全身轨迹规划。在Unitree G1上，SHS实现了高达60 N的有效接触力，而无环境支撑时的最大持续力为13.5 N，同时相对于任务无关的支撑配置，力跟踪性能显著提高。相同的策略无需重新训练即可泛化到不同的任务区域。因此，SHS提供了一种简单机制，可大幅扩展人形机器人的有力操作能力。
英文摘要
Forceful manipulation is challenging for humanoid robots because interaction forces can disturb whole-body balance. We introduce the Supporting Hand Strategy (SHS), which enables a humanoid to brace against the environment with one hand while performing forceful manipulation with the other. SHS optimises a task-conditioned support configuration that guides two synchronous reinforcement-learning policies, without human motion data or online whole-body trajectory planning. On a Unitree G1, SHS achieved usable contact forces up to 60 N, compared with a maximum sustained force of 13.5 N without environmental bracing, while substantially improving force tracking over a task-independent support configuration. The same policies generalised to different task regions without retraining. SHS therefore provides a simple mechanism for substantially extending humanoid forceful-manipulation capability.
CommentsSubmitted to IEEE Robotics and Automation Letters (RA-L). 8 pages, 6 figures. Video demonstration: https://youtu.be/zPrGLxWJzI4. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible
