Summary of Bridging the Communication Gap: Artificial Agents Learning Sign Language Through Imitation, by Federico Tavella et al.
Bridging the Communication Gap: Artificial Agents Learning Sign Language through Imitation
by Federico Tavella, Aphrodite Galata, Angelo Cangelosi
First submitted to arxiv on: 14 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Graphics (cs.GR); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: Artificial agents, particularly humanoid robots, rely on pre-programmed communication methods, limiting their interactions. Our research focuses on acquiring non-verbal communication skills through learning from demonstrations, with potential applications in sign language comprehension and expression. We employ imitation learning for artificial agents, exemplified by teaching a simulated American Sign Language (ASL) using computer vision and deep learning to extract information from videos, and reinforcement learning to enable the agent to replicate observed actions. Our approach eliminates the need for additional hardware, allowing for effective learning of sign language. Specifically, we demonstrate how our methodology successfully teaches 5 different signs involving upper body movements. This research paves the way for advanced communication skills in artificial agents. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Imagine robots and computers that can learn to communicate with us just like humans do! Right now, they usually follow pre-programmed rules, but what if they could learn from examples? That’s what our researchers did. They created a computer program that can teach itself sign language, which is used by people who are deaf or hard of hearing. The program uses computers to watch videos and figure out how to imitate the signs. This means it doesn’t need special hardware to learn. Our team successfully taught the program 5 different signs using just its upper body (arms and hands). This could lead to more advanced communication skills in robots and computers. |
Keywords
* Artificial intelligence * Deep learning * Reinforcement learning
