用概率模型提升机械手多目标操作效率
Multi-Goal Dexterous Hand Manipulation using Probabilistic Model-based Reinforcement Learning
- 设计概率神经网络集成模拟复杂手部动态
- 80分钟内完成立方体三目标姿态操控
- 适合低成本高自由度机械手系统应用
本文针对基于模型的强化学习在多目标灵巧手操作中的挑战，提出目标条件概率模型预测控制（GC-PMPC）。通过构建概率神经网络集成以描述高维灵巧手动力学，并引入异步MPC策略满足真实灵巧手系统的控制频率需求。在四个模拟的Shadow Hand操作场景中，随机生成目标进行大量评估，显示GC-PMPC显著优于现有先进方法。该方法成功使具有12个主动自由度和5个触觉传感器的缆驱灵巧手DexHand 021，在约80分钟交互时间内学会将立方体移动至三个目标姿态，展现了在低成本灵巧手平台上的卓越学习效率与控制性能。
原文摘要 · Abstract (English)
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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