Dexterous Grasping with Real-World Robotic Reinforcement Learning
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866918145252392960 |
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| author | Huang, Dongchi Zhang, Tianle Li, Yihang Zhao, Ling Li, Jiayi Fang, Zhirui Xia, Chunhe He, Xiaodong |
| author_facet | Huang, Dongchi Zhang, Tianle Li, Yihang Zhao, Ling Li, Jiayi Fang, Zhirui Xia, Chunhe He, Xiaodong |
| contents | Dexterous grasping in the real world presents a fundamental and significant challenge for robot learning. The ability to employ affordance-aware poses to grasp objects with diverse geometries and properties in arbitrary scenarios is essential for general-purpose robots. However, existing research predominantly addresses dexterous grasping problems within simulators, which encounter difficulties when applied in real-world environments due to the domain gap between reality and simulation. This limitation hinders their generalizability and practicality in real-world applications. In this paper, we present DexGraspRL, a reinforcement learning (RL) framework that directly trains robots in real-world environments to acquire dexterous grasping skills. Specifically, DexGraspRL consists of two stages: (i) a pretraining stage that pretrains the policy using imitation learning (IL) with a limited set of expert demonstrations; (ii) a fine-tuning stage that refines the policy through direct RL in real-world scenarios. To mitigate the catastrophic forgetting phenomenon arising from the distribution shift between demonstrations and real-world environments, we design a regularization term that balances the exploitation of RL with the preservation of the pretrained policy. Our experiments with real-world tasks demonstrate that DexGraspRL successfully accomplishes diverse dexterous grasping tasks, achieving an average success rate of nearly 92%. Furthermore, by fine-tuning with RL, our method uncovers novel policies, surpassing the IL policy with a 23% reduction in average cycle time. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_04014 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Dexterous Grasping with Real-World Robotic Reinforcement Learning Huang, Dongchi Zhang, Tianle Li, Yihang Zhao, Ling Li, Jiayi Fang, Zhirui Xia, Chunhe He, Xiaodong Robotics Dexterous grasping in the real world presents a fundamental and significant challenge for robot learning. The ability to employ affordance-aware poses to grasp objects with diverse geometries and properties in arbitrary scenarios is essential for general-purpose robots. However, existing research predominantly addresses dexterous grasping problems within simulators, which encounter difficulties when applied in real-world environments due to the domain gap between reality and simulation. This limitation hinders their generalizability and practicality in real-world applications. In this paper, we present DexGraspRL, a reinforcement learning (RL) framework that directly trains robots in real-world environments to acquire dexterous grasping skills. Specifically, DexGraspRL consists of two stages: (i) a pretraining stage that pretrains the policy using imitation learning (IL) with a limited set of expert demonstrations; (ii) a fine-tuning stage that refines the policy through direct RL in real-world scenarios. To mitigate the catastrophic forgetting phenomenon arising from the distribution shift between demonstrations and real-world environments, we design a regularization term that balances the exploitation of RL with the preservation of the pretrained policy. Our experiments with real-world tasks demonstrate that DexGraspRL successfully accomplishes diverse dexterous grasping tasks, achieving an average success rate of nearly 92%. Furthermore, by fine-tuning with RL, our method uncovers novel policies, surpassing the IL policy with a 23% reduction in average cycle time. |
| title | Dexterous Grasping with Real-World Robotic Reinforcement Learning |
| topic | Robotics |
| url | https://arxiv.org/abs/2503.04014 |