Multi-Goal Dexterous Hand Manipulation using Probabilistic Model-based Reinforcement Learning
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912354964340736 |
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| author | Jiang, Yingzhuo Huang, Wenjun Lin, Rongdun Miao, Chenyang Sun, Tianfu Cui, Yunduan |
| author_facet | Jiang, Yingzhuo Huang, Wenjun Lin, Rongdun Miao, Chenyang Sun, Tianfu Cui, Yunduan |
| contents | This paper tackles the challenge of learning multi-goal dexterous hand manipulation tasks using model-based Reinforcement Learning. We propose Goal-Conditioned Probabilistic Model Predictive Control (GC-PMPC) by designing probabilistic neural network ensembles to describe the high-dimensional dexterous hand dynamics and introducing an asynchronous MPC policy to meet the control frequency requirements in real-world dexterous hand systems. Extensive evaluations on four simulated Shadow Hand manipulation scenarios with randomly generated goals demonstrate GC-PMPC's superior performance over state-of-the-art baselines. It successfully drives a cable-driven Dexterous hand, DexHand 021 with 12 Active DOFs and 5 tactile sensors, to learn manipulating a cubic die to three goal poses within approximately 80 minutes of interactions, demonstrating exceptional learning efficiency and control performance on a cost-effective dexterous hand platform. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_21585 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-Goal Dexterous Hand Manipulation using Probabilistic Model-based Reinforcement Learning Jiang, Yingzhuo Huang, Wenjun Lin, Rongdun Miao, Chenyang Sun, Tianfu Cui, Yunduan Robotics Artificial Intelligence Systems and Control This paper tackles the challenge of learning multi-goal dexterous hand manipulation tasks using model-based Reinforcement Learning. We propose Goal-Conditioned Probabilistic Model Predictive Control (GC-PMPC) by designing probabilistic neural network ensembles to describe the high-dimensional dexterous hand dynamics and introducing an asynchronous MPC policy to meet the control frequency requirements in real-world dexterous hand systems. Extensive evaluations on four simulated Shadow Hand manipulation scenarios with randomly generated goals demonstrate GC-PMPC's superior performance over state-of-the-art baselines. It successfully drives a cable-driven Dexterous hand, DexHand 021 with 12 Active DOFs and 5 tactile sensors, to learn manipulating a cubic die to three goal poses within approximately 80 minutes of interactions, demonstrating exceptional learning efficiency and control performance on a cost-effective dexterous hand platform. |
| title | Multi-Goal Dexterous Hand Manipulation using Probabilistic Model-based Reinforcement Learning |
| topic | Robotics Artificial Intelligence Systems and Control |
| url | https://arxiv.org/abs/2504.21585 |