LEGATO: Cross-Embodiment Imitation Using a Grasping Tool
| Autor: | Seo, Mingyo, Park, H. Andy, Yuan, Shenli, Zhu, Yuke, Sentis, Luis |
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| Rok vydání: | 2024 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | Cross-embodiment imitation learning enables policies trained on specific embodiments to transfer across different robots, unlocking the potential for large-scale imitation learning that is both cost-effective and highly reusable. This paper presents LEGATO, a cross-embodiment imitation learning framework for visuomotor skill transfer across varied kinematic morphologies. We introduce a handheld gripper that unifies action and observation spaces, allowing tasks to be defined consistently across robots. Using this gripper, we train visuomotor policies via imitation learning, applying a motion-invariant transformation to compute the training loss. Gripper motions are then retargeted into high-degree-of-freedom whole-body motions using inverse kinematics for deployment across diverse embodiments. Our evaluations in simulation and real-robot experiments highlight the framework's effectiveness in learning and transferring visuomotor skills across various robots. More information can be found at the project page: https://ut-hcrl.github.io/LEGATO. Comment: Submitted to RA-L |
| Databáze: | arXiv |
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