Summary
This chapter explored the integration of ROS 2 with DRL, focusing on NVIDIA Isaac Lab and Isaac Sim. It began by explaining reinforcement learning basics, where agents learn through interaction with environments to maximize rewards. The chapter then introduced Isaac Lab, a framework built on the Isaac Sim simulator for training robot policies. Key sections covered setting up Isaac Lab in Ubuntu 24.04 using Docker, the architecture of the training process, and practical examples of training different robots like the UR-10 robotic arm, AnyMal quadruped, and H1 humanoid robot. The training process involves configuring assets, designing learning tasks, registering with Gymnasium, and running training using GPU-accelerated algorithms.
Finally, the chapter discussed how to deploy trained models to real robots using ROS 2, with a case study of deploying a locomotion model to Boston Dynamics’ Spot robot. Throughout, the text emphasized the workflow from simulation training...