  In order for autonomous mobile robots to navigate in human spaces, they must
abide by our social norms. Reinforcement learning (RL) has emerged as an
effective method to train sequential decision-making policies that are able to
respect these norms. However, a large portion of existing work in the field
conducts both RL training and testing in simplistic environments. This limits
the generalization potential of these models to unseen environments, and the
meaningfulness of their reported results. We propose a method to improve the
generalization performance of RL social navigation methods using curriculum
learning. By employing multiple environment types and by modeling pedestrians
using multiple dynamics models, we are able to progressively diversify and
escalate difficulty in training. Our results show that the use of curriculum
learning in training can be used to achieve better generalization performance
than previous training methods. We also show that results presented in many
existing state-of-the-art RL social navigation works do not evaluate their
methods outside of their training environments, and thus do not reflect their
policies' failure to adequately generalize to out-of-distribution scenarios. In
response, we validate our training approach on larger and more crowded testing
environments than those used in training, allowing for more meaningful
measurements of model performance.
