WARP: 从离线人类演示中学习的全身重定向
WARP: Whole-Body Retargeting for Learning from Offline Human Demonstrations
AI总结：
提出WARP离线管线，通过闭式肩-肘-腕几何求解器精确跟踪末端执行器并保持全身结构意图，实现从人类演示到机器人动作的零样本全身移动操作。
摘要：
从人类演示直接迁移到可学习的机器人动作是实现可扩展全身移动操作的关键步骤。虽然人类数据比移动遥操作更具可扩展性，但需要克服显著的具身差距。现有的重定向方法产生不精确或不一致的解，导致动作多模态性，阻碍监督策略可靠收敛。我们提出全身感知的人体姿态重定向（WARP），一种离线管线，显式建模具身差异以提取精确、唯一的全身动作。WARP利用闭式肩-肘-腕（SEW）几何求解器进行精确的末端执行器跟踪，同时保留全身结构意图。结合惰性移动基座控制，它提取准确、一致的机器人轨迹。评估表明WARP为开环真实世界回放提供高度可靠的数据。据我们所知，WARP是第一个直接从离线人类演示实现零样本全身移动操作的框架，消除了需要人在环中遥操作动作数据的需求。更多详情请见 https://warp-retarget.github.io/
英文摘要：
Direct transfer from human demonstration to learnable robot action is a crucial step towards scalable whole-body mobile manipulation. While human data scales better than mobile teleoperation, it requires overcoming significant embodiment gaps. Existing retargeting methods yield imprecise or inconsistent solutions, causing action multi-modality that prevents supervised policies from reliably converging. We present Whole-body-Aware Retargeting from human Pose (WARP), an offline pipeline that explicitly models embodiment differences to extract precise, unique whole-body actions. WARP leverages a closed-form Shoulder-Elbow-Wrist (SEW) geometric solver for exact end-effector tracking while preserving whole-body structural intent. Paired with lazy mobile-base control, it extracts accurate, consistent robot trajectories. Evaluations show WARP provides highly reliable data for open-loop real-world replay. To our knowledge, WARP is the first framework to achieve zero-shot whole-body mobile manipulation directly from offline human demonstrations, eliminating the need for human-in-the-loop teleoperation action data. More details on https://warp-retargeting.github.io/
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。
