HiWET：分层世界框架末端跟踪用于长周期人形机器人运动与操作
HiWET: Hierarchical World-Frame End-Effector Tracking for Long-Horizon Humanoid Loco-Manipulation
AI总结：
HiWET通过分层强化学习框架实现世界框架下的精确末端跟踪，结合运动学流形先验提升人形机器人长周期运动与操作的稳定性与精度。
摘要：
人形机器人运动与操作需要在基底运动和碰撞影响下保持动态稳定性并执行精确的操作任务。现有方法通常以身体为中心的框架来制定命令，无法内在纠正由腿部运动引起的累积世界框架漂移。我们重新表述该问题为世界框架末端跟踪，并提出HiWET，一种分层强化学习框架，将全局推理与动态执行解耦。高层策略生成子目标，联合优化末端在世界框架中的精度和基底定位，而低层策略在稳定性约束下执行这些命令。我们引入了运动学流形先验（KMP），通过残差学习将操作流形嵌入到动作空间中，减少探索维度并缓解运动学无效行为。广泛的模拟和消融研究证明，HiWET在长周期世界框架任务中实现了精确且稳定的末端跟踪。我们验证了低层策略在物理人形机器人上的零样本仿真到现实转移，展示了在多样化操作命令下稳定的运动。这些结果表明，显式世界框架推理结合分层控制为长周期人形机器人运动与操作提供了一种有效且可扩展的解决方案。
英文摘要：
Humanoid loco-manipulation requires executing precise manipulation tasks while maintaining dynamic stability amid base motion and impacts. Existing approaches typically formulate commands in body-centric frames, fail to inherently correct cumulative world-frame drift induced by legged locomotion. We reformulate the problem as world-frame end-effector tracking and propose HiWET, a hierarchical reinforcement learning framework that decouples global reasoning from dynamic execution. The high-level policy generates subgoals that jointly optimize end-effector accuracy and base positioning in the world frame, while the low-level policy executes these commands under stability constraints. We introduce a Kinematic Manifold Prior (KMP) that embeds the manipulation manifold into the action space via residual learning, reducing exploration dimensionality and mitigating kinematically invalid behaviors. Extensive simulation and ablation studies demonstrate that HiWET achieves precise and stable end-effector tracking in long-horizon world-frame tasks. We validate zero-shot sim-to-real transfer of the low-level policy on a physical humanoid, demonstrating stable locomotion under diverse manipulation commands. These results indicate that explicit world-frame reasoning combined with hierarchical control provides an effective and scalable solution for long-horizon humanoid loco-manipulation.
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。
