学习迁移人类手部技能以用于机器人操作
Learning to Transfer Human Hand Skills for Robot Manipulations
AI总结：
本文提出从人类手部示范学习联合运动流形并生成伪监督三元组，以推断合理机器人动作，显著提升真实机器人灵巧操作的重定向效果。
摘要：
我们提出了一种从人类手部运动示范中向机器人教授灵巧操作任务的方法。现有方法仅依赖运动学信息，而未考虑机器人与物体交互的合理性；与之不同，我们的方法直接从人类运动示范中推断合理的机器人操作动作。为解决人手与机器人系统之间的具身鸿沟，该方法学习一个联合运动流形，在三维空间中映射人手运动、机器人手部动作和物体运动，使我们能够根据其他运动分量推断某一运动分量。其核心思想是生成伪监督三元组，将人类、物体和机器人的运动轨迹进行合成配对。通过机器人手部操作的真实世界实验，我们证明这种数据驱动的重定向方法显著优于传统重定向技术，有效弥合了人手与机器人手之间的具身鸿沟。网站：https://rureadyo.github.io/MocapRobot/。
英文摘要：
We present a method for teaching dexterous manipulation tasks to robots from human hand motion demonstrations. Unlike existing approaches that solely rely on kinematics information without taking into account the plausibility of robot and object interaction, our method directly infers plausible robot manipulation actions from human motion demonstrations. To address the embodiment gap between the human hand and the robot system, our approach learns a joint motion manifold that maps human hand movements, robot hand actions, and object movements in 3D, enabling us to infer one motion component from others. Our key idea is the generation of pseudo-supervision triplets, which pair human, object, and robot motion trajectories synthetically. Through real-world experiments with robot hand manipulation, we demonstrate that our data-driven retargeting method significantly outperforms conventional retargeting techniques, effectively bridging the embodiment gap between human and robotic hands. Website at https://rureadyo.github.io/MocapRobot/.
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。
