单手装配两个零件
Assembling Two Parts in One Hand
- The University of Hong Kong（香港大学）
- The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
- ETH Zurich（苏黎世联邦理工学院）
- The Hong Kong University of Science and Technology（香港科技大学）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
本研究提出强化学习框架，实现单只灵巧手内两刚性零件装配，通过目标相对位姿驱动及辅助奖励塑造手指协调，在模拟训练后零样本迁移至真实硬件，验证了鲁棒性并可作为机器人手基准。
中文摘要 AI 辅助
人类灵巧性的一个标志是手指的协作使用，其中不同的手指承担不同但协调的角色，以完成精细操作，例如用握住笔的手给笔盖帽。我们通过手内装配来研究这种手指级别的协调：在单只灵巧手中配合两个刚性物体，无需第二只手臂或固定装置。我们提出了一种强化学习公式，在统一框架中解决这个问题，该框架由两个零件之间的目标相对位姿驱动。手指协调通过基于功能的辅助奖励塑造，并正则化到单一的人类参考姿态，而域随机化以及历史本体感觉和物体观测的融合赋予了对遮挡引起的估计噪声的鲁棒性。相同的配方解决了三个不同的装配任务（瓶子、注射器和记号笔）。仅在模拟中训练，策略零样本迁移到带单个摄像头的硬件，展示了对由遮挡引起的状态估计误差的鲁棒性。我们的实验还揭示，手内装配对手部形态提出了要求，并可作为现代机器人手系统的基准。视频和代码可在以下网址获取：此https URL。
英文摘要
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/.
