Learning to navigate through crowded environments

TLDR

The study aims to develop planners that enable mobile robots to navigate crowded indoor and outdoor spaces by generating human‑like motion. The authors use inverse reinforcement learning on example paths, extending it to partially observable settings, and validate the method in a realistic crowd‑flow simulator. The resulting planner successfully steers robots along pedestrian flow in crowded settings and defaults to the shortest path when the environment is empty.

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.