Towards Dynamic Obstacle Avoidance for Robot Manipulators with Deep Reinforcement Learning
Publikation aus Robotics
Industrie-Robotersystem-Technologien
Friedemann Zindler, Matteo Lucchi, Lucas Wohlhart, Horst Pichler, Michael Hofbaur
Advances in Service and Industrial Robotics. RAAD 2022. Mechanisms and Machine Science, vol 120. Springer, Cham. , 4/2022
We use reinforcement learning (RL) to demonstrate an easily reproducible setup to learn dynamic obstacle avoidance for a robotic arm based on sensory input as it follows a pre-planned trajectory. Training takes place exclusively in a simulation environment with random obstacle movements around the robot. We show that training dynamic obstacle avoidance in simulation translates well to the real environment with a UR5 manipulator and yields similar performance and success without further tuning of the learned policy. This is a step towards learning general skills needed to enable robots to operate in dynamic environments shared with humans. Source code, data and application videos are available at: https://www.robogym.net.
Keywords: Machine learning for robot control, Collision avoidance, Transfer learning