A3C Based Motion Learning for an Autonomous Mobile Robot in Crowds
- Resource Type
- Conference
- Authors
- Sasaki, Yoko; Matsuo, Syusuke; Kanezaki, Asako; Takemura, Hiroshi
- Source
- 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC) Systems, Man and Cybernetics (SMC), 2019 IEEE International Conference on. :1036-1042 Oct, 2019
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
General Topics for Engineers
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Collision avoidance
Mobile robots
Training
Reinforcement learning
Path planning
Robot motion
- Language
- ISSN
- 2577-1655
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.