Robotics

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

Abstract:

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

Url: https://doi.org/10.1007/978-3-031-04870-8_11