Bridging the Communication Gap: Artificial Agents Learning Sign Language through Imitation

TL;DR

This paper presents a method for teaching simulated humanoid robots to learn and replicate American Sign Language signs using imitation learning, computer vision, and reinforcement learning, without extra hardware, enhancing non-verbal communication capabilities.

Contribution

The study introduces a novel approach combining computer vision and reinforcement learning for sign language acquisition in simulated humanoid agents, avoiding additional hardware requirements.

Findings

Successfully taught 5 different sign language gestures

Demonstrated effective imitation learning for non-verbal communication

Eliminated hardware needs through computer vision and deep learning

Abstract

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.