Can MARL improve bearing-only multi-robot target pursuit?
Cooperative Bearing-Only Target Pursuit via Multiagent Reinforcement Learning: Design and Experiment
This paper tackles the problem of coordinating multiple robots to pursue a moving target using only bearing information (direction), as obtainable from vision sensors. It combines a novel bearing-only state estimation filter with a multi-agent reinforcement learning (MARL) framework for pursuit control. The system is designed for heterogeneous robots, some with omnidirectional movement and others with unicycle-like motion. Sim-to-real techniques, including adjustable low-level control gains and spectral-normalized RL, are implemented to ensure smooth, robust control transferable to real-world robots.
For LLM-based multi-agent systems, the key takeaways are the robust bearing-only state estimation filter and the emphasis on sim-to-real transfer. The filter could be incorporated into LLM agents reliant on noisy or incomplete sensory information. The paper's focus on practical implementation through methods like spectral normalization, which smooths control outputs, addresses common challenges in deploying LLM-based agents in real-world scenarios. The adaptable, model-free MARL approach could be combined with LLMs for higher-level reasoning and decision-making in complex, dynamic environments.