Interpretable Robot Control via Structured Behavior Trees and Large Language Models

2025-08-13

AI Summary

Traditional robot control relies on fixed interfaces or predefined commands, limiting adaptability to dynamic, unstructured environments. This paper proposes a novel control framework integrating large language models (LLMs) with behavior trees (BTs), wherein natural language instructions are parsed into executable and interpretable BTs; it further supports plug-and-play integration of perception modules (e.g., human pose tracking, gesture recognition). The framework combines semantic understanding with flexible task decomposition, significantly enhancing the intuitiveness, scalability, and environmental adaptability of human–robot interaction. In real-world experiments, the system achieves a 94% average accuracy from instruction comprehension to execution. The implementation is open-sourced, demonstrating practical applicability and reproducibility.

Technology Category

• Intelligent Robots: Behavior Learning & Control
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Natural Language Processing: Safety and Robustness

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Develop intuitive HRI interfaces for robots in human environments
• Translate natural language instructions into executable robot actions
• Enhance robot adaptability in dynamic unstructured settings

Innovation

• Combines LLMs with Behavior Trees
• Translates natural language to actions
• Supports modular perception-based plugins