Lab Meeting June 8th, 2010 (fish60): Learning to Navigate Through Crowded Environments

Peter Henry, Christian Vollmer, Brian Ferris, and Dieter Fox,
Learning to Navigate Through Crowded Environments,
in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA2010), Anchorage, Alaska, May 2010

Abstract—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.

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