Bridging the Communication Gap: Artificial Agents Learning Sign Language through Imitation
This work addresses the communication gap for artificial agents like robots, enabling them to interact more naturally with humans, though it is incremental as it builds on existing imitation learning and deep learning techniques.
The researchers tackled the problem of artificial agents lacking non-verbal communication skills by developing an imitation learning approach that teaches a simulated humanoid American Sign Language using computer vision and reinforcement learning, successfully teaching 5 different signs involving the upper body.
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.