Planning with approximate preferences and its application to disambiguating human intentions in navigation

This paper addresses the problem of planning in the presence of humans modeled as dynamic obstacles with multiple hypotheses on their trajectories and actions which can disambiguate between the hypotheses. To solve this problem, we develop and analyze a generalization to the PPCP (Probabilistic Planning with Clear Preferences) algorithm that allows us to efficiently solve problems with approximate preferences on missing information. The approach finds policies with bounded suboptimal expected cost and scales well with the number of people, only disambiguating between the trajectories of people when necessary. We present simulated results as well as experiments on two different physical robots demonstrating the capability of this planner.