让机器人像人一样灵活切换动作，实时稳定完成复杂步态转换。
Switch: Learning Agile Skills Switching for Humanoid Robots
- 构建动作图模型，基于运动相似性规划跨技能切换路径。
- 实测在多种步态间切换成功率超90%，运动模仿精度保持高位。
- 适合需要高动态适应性的仿人机器人研发与智能控制领域。
通过深度强化学习实现全身控制的最新进展，使仿人机器人在真实世界复杂行走任务中取得显著突破。然而，现有方法在不同技能间的灵活切换上仍存在困难，带来安全风险与应用局限。为此，我们提出层级式多技能系统Switch，可在任意时刻实现平滑技能切换。该系统包含三个核心组件：(1) 动作图（SG），基于多技能运动数据中的运动学相似性建立潜在的跨技能切换路径；(2) 在动作图上通过深度强化学习训练的全身跟踪策略；(3) 在线技能调度器，用于驱动跟踪策略以实现鲁棒执行与平滑过渡。当需技能切换或出现显著跟踪偏差时，调度器进行在线图搜索，找出最优可行路径，确保多样行走技能的高效、稳定与实时执行。全面实验表明，Switch使仿人机器人能够以高成功率完成敏捷技能切换，同时保持出色的运动模仿性能。
原文摘要 · Abstract (English)
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.
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