Improving Robotic Arms through Natural Language Processing, Computer Vision, and Edge Computing

TL;DR

This paper presents a prototype system integrating NLP, computer vision, and edge computing to improve assistive robotic arms' responsiveness and interpretative capabilities for complex natural language commands.

Contribution

It introduces a novel edge computing-based framework combining large language models and vision systems for enhanced human-robot interaction in assistive robotics.

Findings

Accurate intent interpretation from verbal commands

Effective object manipulation based on natural language

Reduced latency and offline capability in robotic control

Abstract

This paper introduces a prototype for a new approach to assistive robotics, integrating edge computing with Natural Language Processing (NLP) and computer vision to enhance the interaction between humans and robotic systems. Our proof of concept demonstrates the feasibility of using large language models (LLMs) and vision systems in tandem for interpreting and executing complex commands conveyed through natural language. This integration aims to improve the intuitiveness and accessibility of assistive robotic systems, making them more adaptable to the nuanced needs of users with disabilities. By leveraging the capabilities of edge computing, our system has the potential to minimize latency and support offline capability, enhancing the autonomy and responsiveness of assistive robots. Experimental results from our implementation on a robotic arm show promising outcomes in terms of accurate intent interpretation and object manipulation based on verbal commands. This research lays the groundwork for future developments in assistive robotics, focusing on creating highly responsive, user-centric systems that can significantly improve the quality of life for individuals with disabilities. For video demonstrations and source code, please refer to: https://tinyurl.com/EnhancedArmEdgeNLP.