Improving Generalization in Reinforcement Learning for Social Robot Navigation

The author argues that training RL models in overly homogeneous environments limits their generalizability, proposing curriculum learning and diversification of pedestrian dynamics models to improve performance. By testing RL agents in more challenging environments, the study aims to provide meaningful evaluations of model generalizability.