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Natural Language Human-Robot Interaction: A Modular Framework for Conversational Robot Control using Large Language Models
(Tartu Ülikool, 2025) Leclerc, Julian Rene; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Tehnoloogiainstituut
As robots are increasingly used in everyday situations beyond industrial environments, traditional controller-based interfaces that require specialized training pose significant barriers to widespread adoption. This thesis introduces a modular natural language human-robot interaction system designed for conversational robot control. It integrates large language models with robust robotic frameworks to enhance usability. The system tackles key challenges, such as the reliability of large language models, contextual awareness, and bridging the semantic gap. It includes specialized components for error handling, memory management, and cross-platform deployment through a web interface. Validated on various platforms, including TIAGo and Boston Dynamics Spot, the system demonstrates platform-agnostic functionality and accessibility for non-technical users. This advancement helps democratize robot control through natural language while ensuring operational reliability.