Socially compliant mobile robot navigation via inverse reinforcement learning

TLDR

Mobile robots increasingly operate in human environments and must adhere to socially accepted navigation rules. This work proposes a novel method to model human cooperative navigation behavior. The method represents human motion as a mixture distribution over discrete decisions and trajectory variance, learns its parameters by matching feature expectations via Hamiltonian Monte Carlo, and uses a Voronoi graph to efficiently sample trajectories, enabling imitation or tele‑operated behavior replication. Experiments on a real robot in an office show successful navigation among humans and demonstrate that the approach outperforms existing pedestrian behavior models, with implications for behavioral science and computer graphics.

Abstract

Mobile robots are increasingly populating our human environments. To interact with humans in a socially compliant way, these robots need to understand and comply with mutually accepted rules. In this paper, we present a novel approach to model the cooperative navigation behavior of humans. We model their behavior in terms of a mixture distribution that captures both the discrete navigation decisions, such as going left or going right, as well as the natural variance of human trajectories. Our approach learns the model parameters of this distribution that match, in expectation, the observed behavior in terms of user-defined features. To compute the feature expectations over the resulting high-dimensional continuous distributions, we use Hamiltonian Markov chain Monte Carlo sampling. Furthermore, we rely on a Voronoi graph of the environment to efficiently explore the space of trajectories from the robot’s current position to its target position. Using the proposed model, our method is able to imitate the behavior of pedestrians or, alternatively, to replicate a specific behavior that was taught by tele-operation in the target environment of the robot. We implemented our approach on a real mobile robot and demonstrated that it is able to successfully navigate in an office environment in the presence of humans. An extensive set of experiments suggests that our technique outperforms state-of-the-art methods to model the behavior of pedestrians, which also makes it applicable to fields such as behavioral science or computer graphics.