Search-based Planning with Learned Behaviors for Navigation among Pedestrians

Agent control among pedestrians is often approached in one of the three following ways: using predefined behaviors for agent navigation, learning navigation behaviors from data, or search-based planning on a graph where each edge is a feasible action chosen from a set of predefined actions. While the first approach often produces natural looking motions and the second learns and utilizes complex interactions with pedestrians, both lack global reasoning about how to sequence these behaviors to achieve the overall goal. The third approach, namely search-based planning, does incorporate global reasoning but relies on predefined actions that do not involve any interactions with pedestrians or assume predefined interactions that cannot model complex interactions. This is a significant drawback since many situations such as going through a doorway blocked by other people require complex interactions in order to avoid highly suboptimal behaviors or not being able to get to the goal at all. To this end, we propose a search-based planning framework that constructs and searches a graph wherein each edge can be either a predefined action or a learned behavior. We further extend it to deal with the uncertainty arising from introducing learned behaviors. We present the algorithm, go over its theoretical analysis, and present experimental results.