Learning to Navigate Through Crowded Environments

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.

We utilize a crowd simulator developed at UW.

Mall Scenario

The robot integrates with natural crowd flow in a "mall" setting: (YouTube version)

Lane Formation Scenario

The learned robot planner exhibits lane formation behavior, though it was not trained in this setting. (YouTube version)