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

arXiv: https://arxiv.org/abs/2309.16898 · HTML: https://mlsanity.com/paper/2309.16898/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 30 references that are in the map.
- First posted on arXiv 28 Sep 2023.

## Builds on (most cited first, 5 of 5)

- [Language to Rewards for Robotic Skill Synthesis](https://mlsanity.com/paper/2306.08647/) (2023)
- [Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison](https://mlsanity.com/paper/1910.11006/) (2019)
- [SignBERT: Pre-Training of Hand-Model-Aware Representation for Sign Language Recognition](https://mlsanity.com/paper/2110.05382/) (2021)
- [Natural Language-Assisted Sign Language Recognition](https://mlsanity.com/paper/2303.12080/) (2023)
- [BBC-Oxford British Sign Language Dataset](https://mlsanity.com/paper/2111.03635/) (2021)

## Built on by (most cited first, 5 of 5)

- [Large Language Models for Education: A Survey](https://mlsanity.com/paper/2405.13001/) (2024)
- [Nadine: An LLM-driven Intelligent Social Robot with Affective Capabilities and Human-like Memory](https://mlsanity.com/paper/2405.20189/) (2024)
- [How Can Large Language Models Enable Better Socially Assistive Human-Robot Interaction: A Brief Survey](https://mlsanity.com/paper/2404.00938/) (2024)
- [Leveraging Large Language Models for Accurate Sign Language Translation in Low-Resource Scenarios](https://mlsanity.com/paper/2508.18183/) (2025)
- [A Framework for Low-Latency, LLM-driven Multimodal Interaction on the Pepper Robot](https://mlsanity.com/paper/2603.21013/) (2026)

## Abstract (arXiv)

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.