Map-based Deep Imitation Learning for Obstacle Avoidance

Abstract

Making an optimal decision to avoid obstacles while heading to the goal is one of the fundamental challenges for mobile robots equipped with limited computational resources. In this paper, we present a deep imitation learning algorithm that develops a computationally efficient obstacle avoidance policy based on egocentric local occupancy maps. The trained model embedded with a variant of the value iteration networks is able to provide near-optimal continuous action commands through fast feed-forward inferences and generalize well to unseen planning-based scenarios. To improve the policy robustness, we augment the training data set with artificially generated maps, which effectively alleviates the shortage of catastrophic samples in normal demonstrations. Extensive experiments on a Segway robot show the effectiveness of the proposed approach in terms of solution optimality, robustness as well as computation time.