The goal of this research is to enable mobile robots to navigate through
crowded environments such as indoor shopping malls, airports, or downtown side
walks. The key research question addressed in this paper is how to learn
planners that generate human-like motion behavior. Our approach uses inverse
reinforcement learning (IRL) to learn human-like navigation behavior based on
example paths. Since robots have only limited sensing, we extend existing IRL
methods to the case of partially observable environments. We demonstrate the
capabilities of our approach using a realistic crowd flow simulator in which
we modeled multiple scenarios in crowded environments. We show that our
planner learned to guide the robot along the flow of people when the
environment is crowded, and along the shortest path if no people are around.