  Artificial agents, particularly humanoid robots, interact with their
environment, objects, and people using cameras, actuators, and physical
presence. Their communication methods are often pre-programmed, limiting their
actions and interactions. Our research explores acquiring non-verbal
communication skills through learning from demonstrations, with potential
applications in sign language comprehension and expression. In particular, we
focus on imitation learning for artificial agents, exemplified by teaching a
simulated humanoid American Sign Language. We use computer vision and deep
learning to extract information from videos, and reinforcement learning to
enable the agent to replicate observed actions. Compared to other methods, our
approach eliminates the need for additional hardware to acquire information. We
demonstrate how the combination of these different techniques offers a viable
way to learn sign language. Our methodology successfully teaches 5 different
signs involving the upper body (i.e., arms and hands). This research paves the
way for advanced communication skills in artificial agents.
