Domain Randomization for Learning to Navigate in Human Environments

In shared human-robot environments, effective navigation requires robots to adapt to various pedestrian behaviors encountered in the real world. Most existing deep reinforcement learning algorithms for human-aware robot navigation typically assume that pedestrians adhere to a single walking behavior during training, limiting their practicality/performance in scenarios where pedestrians exhibit various types of behavior. In this work, we propose to enhance the generalization capabilities of human-aware robot navigation by employing Domain Randomization (DR) techniques to train navigation policies on a diverse range of simulated pedestrian behaviors with the hope of better generalization to the real world. We evaluate the effectiveness of our method by comparing the generalization capabilities of a robot navigation policy trained with and without DR, both in simulations and through a real-user study, focusing on adaptability to different pedestrian behaviors, performance in novel environments, and users' perceived comfort, sociability and naturalness. Our findings reveal that the use of DR significantly enhances the robot's social compliance in both simulated and real-life contexts.