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Learning fine-grained movements is a challenging topic in robotics,particularly in the context of robotic hands. One specific instance of thischallenge is the acquisition of fingerspelling sign language in robots. In thispaper, we propose an approach for learning dexterous motor imitation from videoexamples without additional information. To achieve this, we first build a URDFmodel of a robotic hand with a single actuator for each joint. We then leveragepre-trained deep vision models to extract the 3D pose of the hand from RGBvideos. Next, using state-of-the-art reinforcement learning algorithms formotion 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 areference motion. Finally, we demonstrate the generalizability of our approachby testing it on six different tasks, corresponding to fingerspelled letters.Our results show that our approach is able to successfully imitate thesefine-grained movements without additional information, highlighting itspotential for real-world applications in robotics.

Signs of Language: Embodied Sign Language Fingerspelling Acquisition from Demonstrations for Human-Robot Interaction