TopoRetarget：面向灵巧操作的交互保持重定向
TopoRetarget: Interaction-Preserving Retargeting for Dexterous Manipulation
AI总结：
提出TopoRetarget框架，通过稀疏交互图和距离加权拉普拉斯变形，在重定向中保持手-物体交互结构，提升灵巧操作强化学习策略的性能。
摘要：
人类手-物体演示通过参考跟踪为训练灵巧操作强化学习策略提供了密集的参考运动。然而，要将此类演示用于策略学习，重定向必须保留手部姿态和任务相关的手-物体接触结构。否则，接触和可行性伪影会降低下游策略的性能。我们提出TopoRetarget，一种交互保持的重定向框架，它在不同重定向条件下使用单一参数集，同时保持任务相关的手-物体交互，并将人类演示适应到灵巧机器人手。该方法在手和物体关键点上构建稀疏交互图，并优化带有方向一致性、运动学约束和穿透处理的距离加权拉普拉斯变形。评估表明，生成的参考提高了交互保真度和策略学习：TopoRetarget在ContactPose数据集上实现了所有基线中最佳的接触精度和对齐，将笔旋转训练成功率比现有基线方法提高了40.6个百分点，并在立方体重定向和笔旋转任务上实现了对Wuji手硬件的零样本迁移。
英文摘要：
Human hand-object demonstrations provide dense reference motions for training dexterous manipulation reinforcement learning (RL) policies through reference tracking. However, to use such demonstrations for RL policy learning, retargeting must preserve hand pose and task-relevant hand-object contact structure. Otherwise, contact and feasibility artifacts can degrade downstream RL policy performance. We introduce TopoRetarget, an interaction-preserving retargeting framework that uses a single set of parameters across diverse retargeting conditions while maintaining task-relevant hand-object interaction and adapting human demonstrations to dexterous robot hands. The method constructs a sparse interaction graph over hand and object keypoints and optimizes distance-weighted Laplacian deformation with directional consistency, kinematic constraints, and penetration handling. Evaluations show that the generated references improve both interaction fidelity and policy learning: TopoRetarget achieves the best contact precision and alignment over all baselines on the ContactPose Dataset, improves Pen-Spin training success by 40.6 percentage points over the existing baseline methods, and enables zero-shot transfer to Wuji Hand hardware on cube reorientation and pen spinning.
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。
