Learning Dexterous Manipulation with Quantized Hand State
This work addresses a specific bottleneck in robotic manipulation for researchers and practitioners, offering an incremental improvement over existing methods.
The paper tackles the challenge of learning dexterous manipulation in robotics, where high-dimensional hand actions often dominate the action space and compromise arm control, by proposing DQ-RISE, which quantizes hand states to simplify motion prediction and enable balanced arm-hand coordination, resulting in more efficient learning.
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/