ObjRetarget：一个具有拟人化手臂约束和多面体手部建模的物体感知运动重定向框架
ObjRetarget: An Object-Aware Motion Retargeting Framework with Anthropomorphic Arm Constraints and Polyhedral Hand Modeling
AI总结：
提出ObjRetarget框架，用于从人类视频学习机器人灵巧操作，集成拟人化手臂轨迹约束与结构化手-物体几何建模，提升操作成功率与接触稳定性，泛化性好。
摘要：
从人类操作视频中学习机器人灵巧操作，需将人类意图可靠重定向为可执行机器人动作并保持稳定手-物体接触，这是具身智能关键挑战。现有方法有局限，本文提出ObjRetarget框架，整合多种技术提升性能，实验验证其有效性。
英文摘要：
Learning robot dexterous manipulation from human manipulation videos requires reliably retargeting human intent to executable robot actions while maintaining stable hand-object contact, which remains a key challenge in embodied intelligence. Existing retargeting methods often ignore explicit contact modeling or rely on reinforcement learning, resulting in limited accuracy and generalization. To address this, we propose ObjRetarget, a human-to-robot motion retargeting framework for learning robot dexterous manipulation from human videos, which integrates anthropomorphic arm trajectory constraints with structured hand-object geometric modeling. For arm motion, reference trajectories extracted from human videos are used for initialization, followed by anthropomorphic constraints and redundancy-aware optimization to generate natural and accurate movements. For hand manipulation, ObjRetarget represents multi-finger contacts using polytope clusters and preserves contact structure through geometric invariants to improve stability. Experiments on real robots show that ObjRetarget improves manipulation success rates and contact stability across multiple dexterous tasks, and generalizes well to different demonstrations, object poses, and task settings.
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。
