Interpretable Robot Control via Structured Behavior Trees and Large Language Models

TL;DR

This paper introduces a framework combining Large Language Models with Behavior Trees to enable robots to understand natural language commands and perform perception-based actions, improving human-robot interaction in real-world environments.

Contribution

It presents a novel, scalable system that integrates LLMs with behavior trees for natural language understanding and robotic control, focusing on perception functionalities.

Findings

Achieved approximately 94% accuracy in real-world experiments.

Demonstrated practical applicability across diverse environments.

Provided an open-source implementation for the community.

Abstract

As intelligent robots become more integrated into human environments, there is a growing need for intuitive and reliable Human-Robot Interaction (HRI) interfaces that are adaptable and more natural to interact with. Traditional robot control methods often require users to adapt to interfaces or memorize predefined commands, limiting usability in dynamic, unstructured environments. This paper presents a novel framework that bridges natural language understanding and robotic execution by combining Large Language Models (LLMs) with Behavior Trees. This integration enables robots to interpret natural language instructions given by users and translate them into executable actions by activating domain-specific plugins. The system supports scalable and modular integration, with a primary focus on perception-based functionalities, such as person tracking and hand gesture recognition. To evaluate the system, a series of real-world experiments was conducted across diverse environments. Experimental results demonstrate that the proposed approach is practical in real-world scenarios, with an average cognition-to-execution accuracy of approximately 94%, making a significant contribution to HRI systems and robots. The complete source code of the framework is publicly available at https://github.com/snt-arg/robot_suite.