Hierarchical LLMs In-the-loop Optimization for Real-time Multi-Robot Target Tracking under Unknown Hazards
<|reference_start|>Hierarchical LLMs In-the-loop Optimization for Real-time Multi-Robot Target Tracking under Unknown Hazards: In this paper, we propose a hierarchical Large Language Models (LLMs) in-the-loop optimization framework for real-time multi-robot task allocation and target tracking in an unknown hazardous environment subject to sensing and communication attacks. We formulate multi-robot coordination for tracking tasks as a bi-level optimization problem, with LLMs to reason about potential hazards in the environment and the status of the robot team and modify both the inner and outer levels of the optimization. The inner LLM adjusts parameters to prioritize various objectives, including performance, safety, and energy efficiency, while the outer LLM handles online variable completion for team reconfiguration. This hierarchical approach enables real-time adjustments to the robots' behavior. Additionally, a human supervisor can offer broad guidance and assessments to address unexpected dangers, model mismatches, and performance issues arising from local minima. We validate our proposed framework in both simulation and real-world experiments with comprehensive evaluations, which provide the potential for safe LLM integration for multi-robot problems.<|reference_end|>
@article{wu2024hierarchical,
    title={Hierarchical LLMs In-the-loop Optimization for Real-time Multi-Robot
  Target Tracking under Unknown Hazards},
    author={Yuwei Wu, Yuezhan Tao, Peihan Li, Guangyao Shi, Gaurav S. Sukhatmem,
  Vijay Kumar, Lifeng Zhou},
    journal={arXiv preprint arXiv:2409.12274},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.12274},
    primaryClass={cs.RO}
}