LLM-Foraging: Large Language Models for Decentralized Swarm Robot Foraging
Summary
LLM-Foraging is a novel decentralized swarm robot controller that integrates a large language model (LLM) into the Central-Place Foraging Algorithm (CPFA) state machine. This approach augments CPFA's decision-making at three specific points: post-deposit, central-zone arrival, and search starvation. Each robot operates its own LLM client, querying it with only local state information, while the existing CPFA motion and sensing stack executes the LLM-selected action. Unlike traditional methods that rely on computationally expensive offline parameter optimization (e.g., genetic algorithms) for specific configurations, LLM-Foraging functions as a general decision policy, enabling training-free deployment and transferability across varying team sizes (4-10 robots), arena sizes (6x6 to 10x10 meters), and resource distributions (clustered, powerlaw, random). Evaluations in Gazebo with TurtleBot3 robots across 36 configurations demonstrate that LLM-Foraging collects significantly more resources, with a mean relative improvement of +70% and an absolute gain of 22.9 resources per trial, particularly excelling in structured environments.
Key takeaway
Research Scientists developing swarm robotics should consider integrating LLMs at critical decision points within existing state machines to achieve greater adaptability and reduce the need for extensive re-optimization. This approach allows for training-free deployment across diverse environmental conditions and team sizes, significantly outperforming traditional GA-tuned methods. You can improve system consistency and resource collection efficiency, especially in environments with exploitable spatial structures, by leveraging the LLM's general decision policy.
Key insights
Integrating LLMs at key decision points in swarm robotics enhances adaptability and performance without costly re-optimization.
Principles
- Decentralized LLM decision-making improves swarm foraging.
- LLMs can generalize policies across varied environments.
- Augmenting existing state machines is effective.
Method
LLM-Foraging augments the CPFA state machine by querying a local LLM client at post-deposit, central-zone arrival, and search starvation decision points, using local state to select from existing CPFA actions.
In practice
- Deploy LLMs for tactical decisions in swarm robotics.
- Use local state for decentralized LLM queries.
- Implement fallback mechanisms for LLM failures.
Topics
- LLM-Foraging
- Swarm Robotics
- CPFA Algorithm
- Decentralized Control
- Resource Foraging
Best for: Research Scientist, AI Scientist, Robotics Engineer, Machine Learning Engineer
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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.MA updates on arXiv.org.