Learning to Transfer Human Hand Skills for Robot Manipulations
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909451078860800 |
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| author | Park, Sungjae Lee, Seungho Choi, Mingi Lee, Jiye Kim, Jeonghwan Kim, Jisoo Joo, Hanbyul |
| author_facet | Park, Sungjae Lee, Seungho Choi, Mingi Lee, Jiye Kim, Jeonghwan Kim, Jisoo Joo, Hanbyul |
| contents | 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/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_04169 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning to Transfer Human Hand Skills for Robot Manipulations Park, Sungjae Lee, Seungho Choi, Mingi Lee, Jiye Kim, Jeonghwan Kim, Jisoo Joo, Hanbyul Robotics Artificial Intelligence Machine Learning 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/. |
| title | Learning to Transfer Human Hand Skills for Robot Manipulations |
| topic | Robotics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2501.04169 |