A Sign Language Recognition System with Pepper, Lightweight-Transformer, and LLM

TL;DR

This paper presents a lightweight deep learning model for ASL recognition on the Pepper robot, combined with LLMs for natural interaction, demonstrating effective non-verbal communication in real-world scenarios.

Contribution

The study introduces an efficient embedded ASL recognition model and integrates LLMs for natural robot interactions, advancing humanoid-robot communication capabilities.

Findings

Effective sign recognition on embedded systems

Natural co-speech gesture generation demonstrated

Enhanced human-robot interaction in real-world settings

Abstract

This research explores using lightweight deep neural network architectures to enable the humanoid robot Pepper to understand American Sign Language (ASL) and facilitate non-verbal human-robot interaction. First, we introduce a lightweight and efficient model for ASL understanding optimized for embedded systems, ensuring rapid sign recognition while conserving computational resources. Building upon this, we employ large language models (LLMs) for intelligent robot interactions. Through intricate prompt engineering, we tailor interactions to allow the Pepper Robot to generate natural Co-Speech Gesture responses, laying the foundation for more organic and intuitive humanoid-robot dialogues. Finally, we present an integrated software pipeline, embodying advancements in a socially aware AI interaction model. Leveraging the Pepper Robot's capabilities, we demonstrate the practicality and effectiveness of our approach in real-world scenarios. The results highlight a profound potential for enhancing human-robot interaction through non-verbal interactions, bridging communication gaps, and making technology more accessible and understandable.