RL-Driven Data Generation for Robust Vision-Based Dexterous Grasping
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908337345396736 |
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| author | Kanehira, Atsushi Wake, Naoki Sasabuchi, Kazuhiro Takamatsu, Jun Ikeuchi, Katsushi |
| author_facet | Kanehira, Atsushi Wake, Naoki Sasabuchi, Kazuhiro Takamatsu, Jun Ikeuchi, Katsushi |
| contents | This work presents reinforcement learning (RL)-driven data augmentation to improve the generalization of vision-action (VA) models for dexterous grasping. While real-to-sim-to-real frameworks, where a few real demonstrations seed large-scale simulated data, have proven effective for VA models, applying them to dexterous settings remains challenging: obtaining stable multi-finger contacts is nontrivial across diverse object shapes. To address this, we leverage RL to generate contact-rich grasping data across varied geometries. In line with the real-to-sim-to-real paradigm, the grasp skill is formulated as a parameterized and tunable reference trajectory refined by a residual policy learned via RL. This modular design enables trajectory-level control that is both consistent with real demonstrations and adaptable to diverse object geometries. A vision-conditioned policy trained on simulation-augmented data demonstrates strong generalization to unseen objects, highlighting the potential of our approach to alleviate the data bottleneck in training VA models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_18084 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RL-Driven Data Generation for Robust Vision-Based Dexterous Grasping Kanehira, Atsushi Wake, Naoki Sasabuchi, Kazuhiro Takamatsu, Jun Ikeuchi, Katsushi Robotics This work presents reinforcement learning (RL)-driven data augmentation to improve the generalization of vision-action (VA) models for dexterous grasping. While real-to-sim-to-real frameworks, where a few real demonstrations seed large-scale simulated data, have proven effective for VA models, applying them to dexterous settings remains challenging: obtaining stable multi-finger contacts is nontrivial across diverse object shapes. To address this, we leverage RL to generate contact-rich grasping data across varied geometries. In line with the real-to-sim-to-real paradigm, the grasp skill is formulated as a parameterized and tunable reference trajectory refined by a residual policy learned via RL. This modular design enables trajectory-level control that is both consistent with real demonstrations and adaptable to diverse object geometries. A vision-conditioned policy trained on simulation-augmented data demonstrates strong generalization to unseen objects, highlighting the potential of our approach to alleviate the data bottleneck in training VA models. |
| title | RL-Driven Data Generation for Robust Vision-Based Dexterous Grasping |
| topic | Robotics |
| url | https://arxiv.org/abs/2504.18084 |