Learning fine-grained movements is a challenging topic in robotics, particularly in the context of robotic hands. One specific instance of this challenge is the acquisition of fingerspelling sign language in robots. In this paper, we propose an approach for learning dexterous motor imitation from video examples without additional information. To achieve this, we first build a URDF model of a robotic hand with a single actuator for each joint. We then leverage pre-trained deep vision models to extract the 3D pose of the hand from RGB videos. Next, using state-of-the-art reinforcement learning algorithms for motion imitation (namely, proximal policy optimization and soft actor-critic), we train a policy to reproduce the movement extracted from the demonstrations. We identify the optimal set of hyperparameters for imitation based on a reference motion. Finally, we demonstrate the generalizability of our approach by testing it on six different tasks, corresponding to fingerspelled letters. Our results show that our approach is able to successfully imitate these fine-grained movements without additional information, highlighting its potential for real-world applications in robotics.

翻译：学习精细运动是机器人学中的一个挑战性课题，尤其是在机械手领域。这一挑战的具体实例之一是机器人对指拼手语的习得。本文提出一种无需额外信息即可从视频示例中学习灵巧运动模仿的方法。为实现这一点，我们首先构建了一个每个关节配备单个执行器的机械手URDF模型；随后利用预训练的深度视觉模型从RGB视频中提取手部的3D姿态；接着采用当前最先进的运动模仿强化学习算法（即近端策略优化和软演员-评论家算法）训练策略网络，以复现从演示中提取的运动模式。我们基于参考运动确定了用于模仿的最优超参数组合。最后，通过对应六个指拼字母的不同任务验证了方法的泛化能力。实验结果表明，本方法能够在无额外信息的情况下成功模仿这些精细运动，凸显了其在机器人实际应用中的潜力。