Bayesian Optimization Framework for Efficient Fleet Design in Autonomous Multi-Robot Exploration
<|reference_start|>Bayesian Optimization Framework for Efficient Fleet Design in Autonomous Multi-Robot Exploration: This study addresses the challenge of fleet design optimization in the context of heterogeneous multi-robot fleets, aiming to obtain feasible designs that balance performance and costs. In the domain of autonomous multi-robot exploration, reinforcement learning agents play a central role, offering adaptability to complex terrains and facilitating collaboration among robots. However, modifying the fleet composition results in changes in the learned behavior, and training multi-robot systems using multi-agent reinforcement learning is expensive. Therefore, an exhaustive evaluation of each potential fleet design is infeasible. To tackle these hurdles, we introduce Bayesian Optimization for Fleet Design (BOFD), a framework leveraging multi-objective Bayesian Optimization to explore fleets on the Pareto front of performance and cost while accounting for uncertainty in the design space. Moreover, we establish a sub-linear bound for cumulative regret, supporting BOFD's robustness and efficacy. Extensive benchmark experiments in synthetic and simulated environments demonstrate the superiority of our framework over state-of-the-art methods, achieving efficient fleet designs with minimal fleet evaluations.<|reference_end|>
@article{concha2024bayesian,
    title={Bayesian Optimization Framework for Efficient Fleet Design in Autonomous
  Multi-Robot Exploration},
    author={David Molina Concha, Jiping Li, Haoran Yin, Kyeonghyeon Park, Hyun-Rok
  Lee, Taesik Lee, Dhruv Sirohi, Chi-Guhn Lee},
    journal={arXiv preprint arXiv:2408.11751},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.11751},
    primaryClass={cs.RO cs.MA}
}