This paper studies how global dynamics and knowledge of high-level features can inform decision-making for robots in flow-like environments. Specifically, we investigate how coherent sets, an environmental feature found in these environments, inform robot awareness within these scenarios. The proposed approach is an online environmental feature generator which can be used for robot reasoning. We compute coherent sets online with techniques from machine learning and design frameworks for robot behavior that leverage coherent set features. We demonstrate the effectiveness of online methods over offline methods. Notably, we apply these online methods for robot monitoring of pedestrian behaviors and robot navigation through water. Environmental features such as coherent sets provide rich context to robots for smarter, more efficient behavior.

翻译：本文研究全球动态和高层次特征知识如何能为流动式环境中的机器人决策提供依据。 具体地说, 我们调查这些环境中发现的一种环境特征, 如何一致的数据集, 如何在这些情景中告知机器人意识。 提议的方法是一个在线环境特征生成器, 可用于机器人推理。 我们从机器学习和机器人行为设计框架中计算出一致的数据集, 利用一致的数据集特性。 我们展示了在线方法在离线方法上的有效性。 值得注意的是, 我们运用这些在线方法对行人行为和机器人通过水的导航进行机器人监测。 一致的数据集等环境特征为机器人提供了更聪明、更高效的行为提供了丰富的环境。