Human-Robot Cooperative Distribution Coupling for Hamiltonian-Constrained Social Navigation
<|reference_start|>Human-Robot Cooperative Distribution Coupling for Hamiltonian-Constrained Social Navigation: Navigating in human-filled public spaces is a critical challenge for deploying autonomous robots in real-world environments. This paper introduces NaviDIFF, a novel Hamiltonian-constrained socially-aware navigation framework designed to address the complexities of human-robot interaction and socially-aware path planning. NaviDIFF integrates a port-Hamiltonian framework to model dynamic physical interactions and a diffusion model to manage uncertainty in human-robot cooperation. The framework leverages a spatial-temporal transformer to capture social and temporal dependencies, enabling more accurate pedestrian strategy predictions and port-Hamiltonian dynamics construction. Additionally, reinforcement learning from human feedback is employed to fine-tune robot policies, ensuring adaptation to human preferences and social norms. Extensive experiments demonstrate that NaviDIFF outperforms state-of-the-art methods in social navigation tasks, offering improved stability, efficiency, and adaptability.<|reference_end|>
@article{wang2024human-robot,
    title={Human-Robot Cooperative Distribution Coupling for
  Hamiltonian-Constrained Social Navigation},
    author={Weizheng Wang, Chao Yu, Yu Wang, and Byung-Cheol Min},
    journal={arXiv preprint arXiv:2409.13573},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.13573},
    primaryClass={cs.RO}
}