Learning Human Navigation Behavior Using Measured Human Trajectories in Crowded Spaces

Abstract

As humans and mobile robots increasingly coexist in public spaces, their close proximity demands that robots navigate following navigation strategies similar to those exhibited by humans. This could be achieved by learning directly from human demonstration trajectories in a machine learning framework. In this paper, we present a method to learn human navigation behaviors using an imitation learning approach based on generative adversarial imitation learning (GAIL), which has the ability of directly extracting navigation policy. Specifically, we use a large open human trajectory dataset that was experimentally collected in a crowded public space. We then recreate these human trajectories in a 3D robotic simulator, and generate demonstration data using a LIDAR sensor onboard a robot with the robot following the measured human trajectories. We then propose a GAIL based algorithm, which uses occupancy maps generated using LIDAR data as the input, and outputs the navigation policy for robot navigation. Simulation experiments are conducted, and performance evaluation shows that the learned navigation policy generates trajectories qualitatively and quantitatively similar to human trajectories. Compared with existing works using analytical models (such as social force model) to generate human demonstration trajectories, our method learns directly from intrinsic human trajectories, thus exhibits more human-like navigation behaviors.