Switch：为人类机器人学习敏捷技能切换
Switch: Learning Agile Skills Switching for Humanoid Robots
AI总结：
本文提出Switch系统，通过技能图和强化学习实现人形机器人灵活的技能切换，提高运动模仿性能和任务完成效率。
摘要：
近年来，基于深度强化学习的全身控制技术使人形机器人在现实挑战性运动技能上取得了显著进展。然而，现有方法在不同技能间灵活过渡时面临安全性和实用性限制。为此，我们引入了一个分层多技能系统Switch，允许在任何时刻无缝切换技能。我们的方法包括三个关键组件：(1) 一个基于多技能运动数据中运动相似性建立潜在跨技能过渡的技能图(SG)；(2) 通过深度强化学习在该技能图上训练的全身跟踪策略；(3) 一个在线技能调度器，用于驱动跟踪策略以实现稳健的技能执行和流畅的过渡。对于技能切换或显著的跟踪偏差，调度器执行在线图搜索以找到最优可行路径，确保高效、稳定且实时地执行多种运动技能。全面的实验表明，Switch使人类机器人能够以高成功率执行敏捷的技能切换，同时保持强大的运动模仿性能。
英文摘要：
Recent advancements in whole-body control through deep reinforcement learning have enabled humanoid robots to achieve remarkable progress in real-world chal lenging locomotion skills. However, existing approaches often struggle with flexible transitions between distinct skills, cre ating safety concerns and practical limitations. To address this challenge, we introduce a hierarchical multi-skill system, Switch, enabling seamless skill transitions at any moment. Our approach comprises three key components: (1) a Skill Graph (SG) that establishes potential cross-skill transitions based on kinematic similarity within multi-skill motion data, (2) a whole-body tracking policy trained on this skill graph through deep reinforcement learning, and (3) an online skill scheduler to drive the tracking policy for robust skill execution and smooth transitions. For skill switching or significant tracking deviations, the scheduler performs online graph search to find the optimal feasible path, which ensures efficient, stable, and real-time execution of diverse locomotion skills. Comprehensive experiments demonstrate that Switch empowers humanoid to execute agile skill transitions with high success rates while maintaining strong motion imitation performance.
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。
