Hyper-SAMARL: Hypergraph-based Coordinated Task Allocation and Socially-aware Navigation for Multi-Robot Systems
<|reference_start|>Hyper-SAMARL: Hypergraph-based Coordinated Task Allocation and Socially-aware Navigation for Multi-Robot Systems: A team of multiple robots seamlessly and safely working in human-filled public environments requires adaptive task allocation and socially-aware navigation that account for dynamic human behavior. Current approaches struggle with highly dynamic pedestrian movement and the need for flexible task allocation. We propose Hyper-SAMARL, a hypergraph-based system for multi-robot task allocation and socially-aware navigation, leveraging multi-agent reinforcement learning (MARL). Hyper-SAMARL models the environmental dynamics between robots, humans, and points of interest (POIs) using a hypergraph, enabling adaptive task assignment and socially-compliant navigation through a hypergraph diffusion mechanism. Our framework, trained with MARL, effectively captures interactions between robots and humans, adapting tasks based on real-time changes in human activity. Experimental results demonstrate that Hyper-SAMARL outperforms baseline models in terms of social navigation, task completion efficiency, and adaptability in various simulated scenarios.<|reference_end|>
@article{wang2024hyper-samarl:,
    title={Hyper-SAMARL: Hypergraph-based Coordinated Task Allocation and
  Socially-aware Navigation for Multi-Robot Systems},
    author={Weizheng Wang, Aniket Bera, and Byung-Cheol Min},
    journal={arXiv preprint arXiv:2409.11561},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.11561},
    primaryClass={cs.RO cs.MA}
}