Safe POMDP Online Planning among Dynamic Agents via Adaptive Conformal Prediction
<|reference_start|>Safe POMDP Online Planning among Dynamic Agents via Adaptive Conformal Prediction: Online planning for partially observable Markov decision processes (POMDPs) provides efficient techniques for robot decision-making under uncertainty. However, existing methods fall short of preventing safety violations in dynamic environments. This work presents a novel safe POMDP online planning approach that maximizes expected returns while providing probabilistic safety guarantees amidst environments populated by multiple dynamic agents. Our approach utilizes data-driven trajectory prediction models of dynamic agents and applies Adaptive Conformal Prediction (ACP) to quantify the uncertainties in these predictions. Leveraging the obtained ACP-based trajectory predictions, our approach constructs safety shields on-the-fly to prevent unsafe actions within POMDP online planning. Through experimental evaluation in various dynamic environments using real-world pedestrian trajectory data, the proposed approach has been shown to effectively maintain probabilistic safety guarantees while accommodating up to hundreds of dynamic agents.<|reference_end|>
@article{sheng2024safe,
    title={Safe POMDP Online Planning among Dynamic Agents via Adaptive Conformal
  Prediction},
    author={Shili Sheng, Pian Yu, David Parker, Marta Kwiatkowska, and Lu Feng},
    journal={arXiv preprint arXiv:2404.15557},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.15557},
    primaryClass={cs.RO}
}