Learning socially normative robot navigation behaviors with Bayesian inverse reinforcement learning

Mobile robots that navigate in populated environments require the capacity to move efficiently, safely and in human-friendly ways. In this paper, we address this task using a learning approach that enables a mobile robot to acquire navigation behaviors from demonstrations of socially normative human behavior. In the past, such approaches have been typically used to learn only simple behaviors under relatively controlled conditions using rigid representations or with methods that scale poorly to large domains. We thus develop a flexible graph-based representation able to capture relevant task structure and extend Bayesian inverse reinforcement learning to use sampled trajectories from this representation. In experiments with a real robot and a large-scale pedestrian simulator, we are able to show that the approach enables a robot to learn complex navigation behaviors of varying degrees of social normativeness using the same set of simple features.