Assembling Two Parts in One Hand

2026-09-09

AI Summary

研究通过强化学习解决单手组装两个刚性物体的问题，使用基于目标相对位置的方法，并通过领域随机化和历史感知融合增强鲁棒性。

Abstract

A hallmark of human dexterity is the cooperative use of fingers, where different fingers take on distinct yet coordinated roles to accomplish fine manipu- lation, such as capping a pen with the hand that holds it. We study this finger-level coordination through in-hand assembly: mating two rigid objects within a single dexterous hand, with no second arm and no fixture. We present a reinforcement learning formulation to solve this problem in a unified framework, which is driven by a goal relative pose between the two parts. Finger coordination is shaped by a function-based auxiliary reward and regularized toward a single human reference pose, while domain randomization and a fusion of historical proprioception and object observation confer robustness to occlusion-induced estimation noise. The same recipe solves three different assembly tasks (Bottle, Syringe, and Marker). Trained purely in simulation, the policies transfer zero-shot to hardware with a single camera, demonstrating robustness to state-estimation errors caused by oc- clusion. Our experiments also reveal that in-hand assembly places demands on hand morphology and can serve as a benchmark for modern robotic hand systems. Videos and code are available at https://ltbgbird.github.io/in-hand-assembly-page/.

Problem

• in-hand assembly
• finger coordination
• reinforcement learning
• dexterous manipulation
• rigid objects

Innovation

• reinforcement learning
• finger coordination
• auxiliary reward
• domain randomization
• robustness to occlusion