Towards a Configurable and Reusable RL Training Infrastructure for AMRs in ROS2

Pauline Steffel, Jürgen Bock, Alexander Schiendorfer

Abstract: The navigation of AMRs (Autonomous Mobile Robots) within factories, especially when facing moving and changing obstacles, still proves to be a challenge. ROS2 offers promising open-source behaviors that facilitate easier programming. When trying to implement reinforcement learning algorithms that aim at providing more robust policys, one is still faced with a difficult stack of technological layers. We provide an exemplary setup that shows the interplay between ROS2 (Robot Operating System 2) Humble and Reinforcement Learning. This setup works with different robot simulations, and we investigate how well a policy trained on one robot can be transferred to another.

External IDs:dblp:conf/etfa/SteffelBS25