通过量化手部状态提升机器人灵巧操作的协同控制能力
Learning Dexterous Manipulation with Quantized Hand State
- 将手部状态量化以简化动作预测，避免高维动作主导
- 引入连续松弛机制，实现手臂与手部动作联合优化
- 显著改善学习平衡性，适合复杂灵巧操作任务
灵巧机器人手可执行需精细控制和适应性的复杂操作，但其高自由度导致手臂与手部运动紧密耦合，学习与控制困难。成功操作不仅依赖精确的手部动作，还需准确的臂部空间定位及协调的臂手动力学。现有视觉运动策略常将臂手动作合并表示，导致高维手部动作主导联合动作空间，削弱臂部控制。为此，本文提出DQ-RISE，通过量化手部状态简化手部动作预测，并采用连续松弛机制使臂部动作能与紧凑的手部状态共同扩散。该设计使策略能从数据中学习臂手协调，同时防止手部动作过度占据动作空间。实验表明，DQ-RISE实现了更均衡高效的训练，为结构化、可泛化的灵巧操作铺平道路。
原文摘要 · Abstract (English)
Dexterous robotic hands enable robots to perform complex manipulations that require fine-grained control and adaptability. Achieving such manipulation is challenging because the high degrees of freedom tightly couple hand and arm motions, making learning and control difficult. Successful dexterous manipulation relies not only on precise hand motions, but also on accurate spatial positioning of the arm and coordinated arm-hand dynamics. However, most existing visuomotor policies represent arm and hand actions in a single combined space, which often causes high-dimensional hand actions to dominate the coupled action space and compromise arm control. To address this, we propose DQ-RISE, which quantizes hand states to simplify hand motion prediction while preserving essential patterns, and applies a continuous relaxation that allows arm actions to diffuse jointly with these compact hand states. This design enables the policy to learn arm-hand coordination from data while preventing hand actions from overwhelming the action space. Experiments show that DQ-RISE achieves more balanced and efficient learning, paving the way toward structured and generalizable dexterous manipulation. Project website: http://rise-policy.github.io/DQ-RISE/
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