A3C Based Motion Learning for an Autonomous Mobile Robot in Crowds
Yoko Sasaki, Syusuke Matsuo, Asako Kanezaki, Hiroshi Takemura · 2019
The paper proposes a motion planning method using a deep reinforcement learning algorithm, Asynchronous Advantage Actor-Critic (A3C). For mobile robot navigation tasks in crowds, existing path planning based approaches are limited because the surrounding environments change dynamically. The correct motion in such a dynamic environment is underspecified, and a reinforcement learning approach is suitable for generating applicable motion. We propose an A3C based motion planning method for acquiring robot motion for a robot moving through crowds. The proposed method is evaluated in simulated crowds of pedestrians. The experiment section shows the basic performance depending on training parameters and some generated motion examples in the simulator. The learning results using real pedestrian motion are also shown.