Multi-Goal Dexterous Hand Manipulation using Probabilistic Model-based Reinforcement Learning

• URL: http://arxiv.org/abs/2504.21585v1
• Date: Wed, 30 Apr 2025 12:44:38 GMT
• Title: Multi-Goal Dexterous Hand Manipulation using Probabilistic Model-based Reinforcement Learning
• Authors: Yingzhuo Jiang, Wenjun Huang, Rongdun Lin, Chenyang Miao, Tianfu Sun, Yunduan Cui,
• Abstract summary: This paper tackles the challenge of learning multi-goal dexterous hand manipulation tasks using model-based Reinforcement Learning.<n>We propose Goal-Conditioned Probabilistic Model Predictive Control (GC-PMPC) to describe the high-dimensional dexterous hand dynamics.<n>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.
• Score: 2.34860173297653
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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