  In recent years, Deep Reinforcement Learning (DRL) has emerged as a promising
method for robot collision avoidance. However, such DRL models often come with
limitations, such as adapting effectively to structured environments containing
various pedestrians. In order to solve this difficulty, previous research has
attempted a few approaches, including training an end-to-end solution by
integrating a waypoint planner with DRL and developing a multimodal solution to
mitigate the drawbacks of the DRL model. However, these approaches have
encountered several issues, including slow training times, scalability
challenges, and poor coordination among different models. To address these
challenges, this paper introduces a novel approach called evolutionary
curriculum training to tackle these challenges. The primary goal of
evolutionary curriculum training is to evaluate the collision avoidance model's
competency in various scenarios and create curricula to enhance its
insufficient skills. The paper introduces an innovative evaluation technique to
assess the DRL model's performance in navigating structured maps and avoiding
dynamic obstacles. Additionally, an evolutionary training environment generates
all the curriculum to improve the DRL model's inadequate skills tested in the
previous evaluation. We benchmark the performance of our model across five
structured environments to validate the hypothesis that this evolutionary
training environment leads to a higher success rate and a lower average number
of collisions. Further details and results at our project website.
