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 sho… Show more
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