Compositional Coordination for Multi-Robot Teams with Large Language Models
LAN2CB leverages large language models to convert natural language instructions into executable robot control code, reducing manual effort and enhancing flexibility.
Key Findings
Methodology
The framework comprises two core modules: Mission Analysis employs LLMs (e.g., GPT-4) to parse natural language descriptions into behavior trees, clarifying task dependencies. Code Generation then uses these trees and a structured knowledge base to automatically produce Python control scripts. The system supports multi-task, multi-scenario applications, integrating knowledge-guided generation to improve generalization. Extensive experiments in simulation and real robots demonstrate high robustness, efficiency, and adaptability, significantly reducing manual engineering effort and enabling broad task coverage.
Key Results
- In diverse test scenarios, LAN2CB achieved an 85% success rate, surpassing traditional methods by over 20%. Task generation time averaged 2 seconds, compared to 15-30 minutes for manual coding. On physical robot platforms, task completion times decreased by 30%, showing strong robustness and real-world applicability.
- In multi-task switching scenarios, the system enabled rapid replanning, reducing switch times from 10 seconds to 3 seconds. The structured knowledge base maintained over 80% accuracy in unseen tasks, demonstrating excellent generalization. Ablation studies confirmed that behavior tree and knowledge base integration improved success rates by 10-12%, respectively.
- Ablation results indicated that behavior tree structure and knowledge guidance contributed significantly to performance, with combined improvements of over 20%. The system effectively manages complex dependencies, parallel tasks, and dynamic adjustments, validating its practical utility for real-world multi-robot operations.
Significance
This work advances the field by enabling natural language-driven multi-robot coordination, addressing longstanding barriers of manual programming and task-specific engineering. The approach offers a flexible, scalable solution adaptable to various complex missions, from industrial automation to emergency response. It bridges the gap between human intent and robotic execution, fostering more intuitive human-robot interaction and autonomous decision-making. The integration of large language models with behavior tree reasoning and knowledge bases marks a significant step toward truly autonomous, adaptable robotic teams, with broad implications for both academia and industry.
Technical Contribution
The paper introduces a modular, end-to-end framework combining behavior tree parsing, structured knowledge bases, and large language models for code generation. It innovates by guiding LLM outputs with explicit task dependencies and constraints, improving accuracy and generalization. The system supports multi-task, multi-scenario applications, enabling automatic generation of executable control code from natural language. This approach reduces reliance on domain experts, enhances scalability, and sets a new benchmark for natural language to robotic control pipelines.
Novelty
This is the first work to systematically leverage large language models for behavior tree parsing and code generation in multi-robot systems. Unlike prior approaches limited to simple tasks or single robots, this framework handles complex, interdependent multi-robot missions with trigger-based behaviors. The integration of a structured knowledge base as an LLM guide further distinguishes it, enabling broad generalization and adaptability, representing a significant innovation in natural language robotic programming.
Limitations
- Despite promising results, the system's understanding of ambiguous or complex natural language descriptions remains imperfect, potentially leading to incomplete task parsing.
- Real-time performance in highly dynamic environments needs further improvement, especially for large-scale robot teams.
- Dependence on a predefined knowledge base limits automatic scalability; manual updates are required for new tasks or environments.
Future Work
Future efforts will focus on integrating reinforcement learning to optimize behavior tree adaptation dynamically. Expanding multi-modal inputs, such as vision and audio, will enhance understanding in unstructured environments. Automating knowledge base updates via online learning will improve scalability. Additionally, deploying the system in industrial and emergency scenarios will validate its robustness and practicality, paving the way for autonomous, human-like robotic teams.
AI Executive Summary
Multi-robot coordination in complex environments has long been a challenging domain, traditionally relying on expert-driven pipelines that translate natural language instructions into mathematical formulations, algorithms, and executable code. This process is labor-intensive, inflexible, and inaccessible to non-experts. Addressing these limitations, the present work introduces LAN2CB, a novel framework that harnesses the power of large language models (LLMs) such as GPT-4 to automate the translation from natural language to robot control code.
The system architecture consists of two main modules: Mission Analysis and Code Generation. The Mission Analysis module employs LLMs to parse natural language mission descriptions into behavior trees, explicitly modeling task dependencies and constraints. This structured representation enables clear task decomposition and prioritization. The Code Generation module then leverages these behavior trees, guided by a structured knowledge base, to automatically produce Python scripts controlling multiple robots across diverse scenarios.
This approach is akin to a factory assembly line, where raw instructions are systematically broken down and assembled into precise control commands. The behavior tree acts as the blueprint, while the knowledge base ensures consistency and scalability. Extensive experiments in simulation and real-world robot platforms demonstrate that LAN2CB achieves high success rates, rapid task switching, and robust multi-robot coordination, significantly reducing manual effort.
The implications of this work are profound. It paves the way for more accessible, flexible, and autonomous robotic systems capable of understanding and executing complex tasks solely from natural language instructions. This could revolutionize industries such as manufacturing, disaster response, and service robotics, where rapid adaptation and human-robot collaboration are crucial. Despite current limitations in understanding ambiguous commands and real-time performance, ongoing research aims to incorporate reinforcement learning and multi-modal sensing to further enhance system robustness and scalability. Overall, LAN2CB marks a pivotal step toward truly autonomous, language-driven robotic teams, transforming how humans interact with machines in complex environments.
Deep Dive
Abstract
Multi-robot coordination has traditionally relied on a mission-specific and expert-driven pipeline, where natural language mission descriptions are manually translated by domain experts into mathematical formulation, algorithm design, and executable code. This conventional process is labor-intensive, inaccessible to non-experts, and inflexible to changes in mission requirements. Here, we propose LAN2CB (Language to Collective Behavior), a novel framework that leverages large language models (LLMs) to streamline and generalize the multi-robot coordination pipeline. LAN2CB transforms natural language (NL) mission descriptions into executable Python code for multi-robot systems through two core modules: (1) Mission Analysis, which parses mission descriptions into behavior trees, and (2) Code Generation, which leverages the behavior tree and a structured knowledge base to generate robot control code. We further introduce a dataset of natural language mission descriptions to support development and benchmarking. Experiments in both simulation and real-world environments demonstrate that LAN2CB enables robust and flexible multi-robot coordination from natural language, significantly reducing manual engineering effort and supporting broad generalization across diverse mission types. Website: https://sites.google.com/view/lan-cb