  This paper introduces a "proof of concept" 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.
