  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.
