基于人类运动数据的以物体为中心的灵巧操作
Object-Centric Dexterous Manipulation from Human Motion Data
AI总结：
提出一种利用人类手部运动数据训练以物体为中心的灵巧机器人操作的分层策略框架，通过高层轨迹生成和低层手指控制，在10个物体上表现优越并具备泛化能力。
摘要：
操作物体以达到期望的目标状态是灵巧操作的一项基本但重要的技能。人类手部运动展示了熟练的操作能力，为训练多指机器手提供了宝贵的数据。尽管有这种潜力，但由于人类和机器手之间的具身差距，带来了巨大的挑战。在这项工作中，我们引入了一个分层策略学习框架，利用人类手部运动数据来训练以物体为中心的灵巧机器人操作。我们方法的核心是一个高层轨迹生成模型，该模型使用大规模人类手部运动捕捉数据集进行学习，以合成基于期望物体目标状态的人类类似手腕运动。在生成的手腕运动指导下，进一步使用深度强化学习来训练一个低层手指控制器，该控制器基于机器人的具身性，以物理方式与物体交互以实现目标。通过在10个家庭物体上的广泛评估，我们的方法不仅展示了优越的性能，还展示了对新物体几何形状和目标状态的泛化能力。此外，我们将学习到的策略从仿真转移到真实世界的双臂灵巧机器人系统，进一步展示了其在现实世界场景中的适用性。项目网站：https://cypypccpy.github.io/obj-dex.github.io/。
英文摘要：
Manipulating objects to achieve desired goal states is a basic but important skill for dexterous manipulation. Human hand motions demonstrate proficient manipulation capability, providing valuable data for training robots with multi-finger hands. Despite this potential, substantial challenges arise due to the embodiment gap between human and robot hands. In this work, we introduce a hierarchical policy learning framework that uses human hand motion data for training object-centric dexterous robot manipulation. At the core of our method is a high-level trajectory generative model, learned with a large-scale human hand motion capture dataset, to synthesize human-like wrist motions conditioned on the desired object goal states. Guided by the generated wrist motions, deep reinforcement learning is further used to train a low-level finger controller that is grounded in the robot's embodiment to physically interact with the object to achieve the goal. Through extensive evaluation across 10 household objects, our approach not only demonstrates superior performance but also showcases generalization capability to novel object geometries and goal states. Furthermore, we transfer the learned policies from simulation to a real-world bimanual dexterous robot system, further demonstrating its applicability in real-world scenarios. Project website: https://cypypccpy.github.io/obj-dex.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。
