DexH2R：从人类到机器人的任务导向灵巧操作
DexH2R: Task-oriented Dexterous Manipulation from Human to Robots
AI总结：
DexH2R通过结合人类手部运动重定向与任务导向的残差动作策略，提升机器人灵巧操作性能，实现高泛化能力的任务完成。
摘要：
灵巧操作是人类能力的关键方面，使人们能够与各种各样的物体进行交互。最近在从人类示范中学习和远程操作方面的进步使机器人在这一能力上取得了进展。然而，这些方法要么需要复杂的数据收集，如昂贵的人力努力进行眼-机器人接触，要么在面对新场景时泛化能力差。为了解决这两个挑战，我们提出了一种框架DexH2R，结合了人类手部运动重定向与任务导向的残差动作策略，通过弥合人类和机器人灵巧手之间的身体差距来提高任务性能。具体而言，DexH2R直接从重定向的原始动作和任务导向的奖励中学习残差策略，消除了对劳动密集型远程操作系统的需要。此外，我们通过纳入人类手部和物体的期望轨迹来指导新场景的测试，使灵巧手能够通过高泛化能力获得新技能。在模拟和现实环境中进行的大量实验证明了我们工作的有效性，在各种设置中均优于现有最先进的方法，提升了40%。
英文摘要：
Dexterous manipulation is a critical aspect of human capability, enabling interaction with a wide variety of objects. Recent advancements in learning from human demonstrations and teleoperation have enabled progress for robots in such ability. However, these approaches either require complex data collection such as costly human effort for eye-robot contact, or suffer from poor generalization when faced with novel scenarios. To solve both challenges, we propose a framework, DexH2R, that combines human hand motion retargeting with a task-oriented residual action policy, improving task performance by bridging the embodiment gap between human and robotic dexterous hands. Specifically, DexH2R learns the residual policy directly from retargeted primitive actions and task-oriented rewards, eliminating the need for labor-intensive teleoperation systems. Moreover, we incorporate test-time guidance for novel scenarios by taking in desired trajectories of human hands and objects, allowing the dexterous hand to acquire new skills with high generalizability. Extensive experiments in both simulation and real-world environments demonstrate the effectiveness of our work, outperforming prior state-of-the-arts by 40% across various 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。
