mimic-one: a Scalable Model Recipe for General Purpose Robot Dexterity
Fuente:
arXiv
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| Autori principali: | , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866913892431560704 |
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| author | Nava, Elvis Montesinos, Victoriano Bauer, Erik Forrai, Benedek Pai, Jonas Weirich, Stefan Gravert, Stephan-Daniel Wand, Philipp Polinski, Stephan Grewe, Benjamin F. Katzschmann, Robert K. |
| author_facet | Nava, Elvis Montesinos, Victoriano Bauer, Erik Forrai, Benedek Pai, Jonas Weirich, Stefan Gravert, Stephan-Daniel Wand, Philipp Polinski, Stephan Grewe, Benjamin F. Katzschmann, Robert K. |
| contents | We present a diffusion-based model recipe for real-world control of a highly dexterous humanoid robotic hand, designed for sample-efficient learning and smooth fine-motor action inference. Our system features a newly designed 16-DoF tendon-driven hand, equipped with wide angle wrist cameras and mounted on a Franka Emika Panda arm. We develop a versatile teleoperation pipeline and data collection protocol using both glove-based and VR interfaces, enabling high-quality data collection across diverse tasks such as pick and place, item sorting and assembly insertion. Leveraging high-frequency generative control, we train end-to-end policies from raw sensory inputs, enabling smooth, self-correcting motions in complex manipulation scenarios. Real-world evaluations demonstrate up to 93.3% out of distribution success rates, with up to a +33.3% performance boost due to emergent self-correcting behaviors, while also revealing scaling trends in policy performance. Our results advance the state-of-the-art in dexterous robotic manipulation through a fully integrated, practical approach to hardware, learning, and real-world deployment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_11916 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | mimic-one: a Scalable Model Recipe for General Purpose Robot Dexterity Nava, Elvis Montesinos, Victoriano Bauer, Erik Forrai, Benedek Pai, Jonas Weirich, Stefan Gravert, Stephan-Daniel Wand, Philipp Polinski, Stephan Grewe, Benjamin F. Katzschmann, Robert K. Robotics We present a diffusion-based model recipe for real-world control of a highly dexterous humanoid robotic hand, designed for sample-efficient learning and smooth fine-motor action inference. Our system features a newly designed 16-DoF tendon-driven hand, equipped with wide angle wrist cameras and mounted on a Franka Emika Panda arm. We develop a versatile teleoperation pipeline and data collection protocol using both glove-based and VR interfaces, enabling high-quality data collection across diverse tasks such as pick and place, item sorting and assembly insertion. Leveraging high-frequency generative control, we train end-to-end policies from raw sensory inputs, enabling smooth, self-correcting motions in complex manipulation scenarios. Real-world evaluations demonstrate up to 93.3% out of distribution success rates, with up to a +33.3% performance boost due to emergent self-correcting behaviors, while also revealing scaling trends in policy performance. Our results advance the state-of-the-art in dexterous robotic manipulation through a fully integrated, practical approach to hardware, learning, and real-world deployment. |
| title | mimic-one: a Scalable Model Recipe for General Purpose Robot Dexterity |
| topic | Robotics |
| url | https://arxiv.org/abs/2506.11916 |