Deep Reactive Planning in Dynamic Environments

Abstract

4 AIST Figure 1: Our proposed agent learns an end-to-end reactive planning technique by combining traditional path planning algorithms, supervised learning (SL) and reinforcement learning (RL) algorithms in a synergistic way. A deep CNN is used to learn the sequence of waypoints obtained from a kinematic planning algorithm (e.g., a Bidirectional RRT*) given a depth image of the environment. The agent learns to follow arbitrary waypoints using path-conditioned RL, thus resulting in efficient exploration. We show that our trained agent can achieve good sample efficiency, as well as generalization to novel environments in simulation as well as real environments. The whole learning process is done in the simulator by learning a Real2Sim transfer function to make the training process efficient and suitable for robotic systems.