ROS 2 and Deep Reinforcement Learning
In the previous chapter, we discussed LLM and building AI agents. This chapter will discuss the interfacing of reinforcement learning algorithms for robots using ROS 2, specifically deep reinforcement learning. We will discuss how to train, test, and deploy Deep Reinforcement Learning (DRL) algorithms and libraries and interface to robots using ROS 2. The primary focus of the chapter will be introducing NVIDIA Isaac Lab, an open-source, unified framework for robot learning that helps developers train, test, and deploy robot learning policies. By combining the NVIDIA Isaac Sim simulator and Isaac Lab, developers can train various robots like robotic arms, wheeled robots, legged robots, and humanoids.
We will discuss the training and testing of robotic arms, legged robots, and humanoid robots using Isaac Sim and Isaac Lab and interfacing to hardware using ROS 2.
In this chapter, we’re going to cover the following main topics:
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