Abstract
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 offers 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) for assessing 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.
Abstract (translated)
基于部分可观测的马尔可夫决策过程（POMDP）的在线规划为机器人在不确定性环境中的决策提供了有效的技术。然而，现有的方法尚不能在动态环境中防止安全违规。本文提出了一种新的安全POMDP在线规划方法，能在充满多个动态代理人的环境中提供概率安全保证。我们的方法利用动态代理数据的基于数据驱动的轨迹预测模型，并应用自适应收缩预测（ACP）来评估这些预测的不确定性。通过使用获得的ACP基于轨迹预测，我们的方法在在线规划过程中动态地构建安全屏蔽以防止不安全行为。通过使用真实世界行人轨迹数据在各种动态环境中进行实验评估，该方法已被证明在容纳多达数百个动态代理人的情况下，有效保持概率安全保证。
URL
https://arxiv.org/abs/2404.15557
