发表机构
Duke University（杜克大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对人形机器人物理交互中的柔顺性问题，提出结合方向性可调末端执行器柔顺性与可选根部柔顺性的框架，利用分层强化学习控制，实现方向刚度控制、在线调整及协作搬运等能力。
AI 中文摘要
人形机器人越来越能够跟踪复杂的全身运动，但物理交互带来了不同的挑战。当机器人与人或环境接触时，它需要响应外部力，同时保持任务所需的运动。这种响应在末端执行器和身体上的不同方向上可能有所不同。例如，末端执行器可能需要在某个方向上适应接触力，同时在另一个方向上保持运动精度，而机器人身体可能抵抗外部力或随其移动。我们提出了一种用于人形机器人移动操作的柔顺性框架，该框架将方向性可调末端执行器（EE）柔顺性与可选择的根部柔顺性相结合，以实现外部力抑制或力跟随。一个分层强化学习控制器通过高层EE和根部命令调制固定的全身跟踪策略，而交互力则从本体感受历史中估计。我们在人形机器人上的仿真和真实世界实验展示了方向性刚度控制、在线刚度调整、不同的根部柔顺性、柔顺操作和协作搬运。
英文摘要
Humanoids are increasingly capable of tracking complex whole-body motions, but physical interaction introduces a different challenge. When a robot makes contact with a person or the environment, it needs to respond to external forces while preserving the motion needed for the task. This response can vary across directions in the end-effectors and on the body. For example, an end effector may need to accommodate contact force in one direction while maintaining motion accuracy in another, while the robot body may resist an external force or move with it. We present a compliance framework for humanoid loco-manipulation that combines directional and tunable end-effector (EE) compliance with selectable root compliance for external force rejection or force following. A hierarchical reinforcement learning controller modulates a fixed whole-body tracking policy through high-level EE and root commands, while interaction forces are estimated from proprioceptive history. Our simulation and real-world experiments on a humanoid demonstrate directional stiffness control, online stiffness adjustment, distinct root compliance, compliant manipulation, and collaborative carrying.
Comments8 Pages, 5 figures
