In this tutorial you will learn to use the ROS2learn framework, which uses gym-gazebo2 to create OpenAi Gym compliant environments. The video shows the learning process of one of the MARA robotic arm environments created for gym-gazebo2, MARAOrient. In this environment the is goal to learn a trajectory that approximates a point in the 3D space with a certain trajectory.
The goal of this 5 minute video is to quickly explain how the typical robot training looks like, while explaining as many concepts as possible. In this video you will learn:
This quick ROS2Learn tutorial introduces the concepts of transfer learning and multi-instance. We show how to resume a training from a saved checkpoint and we demonstrate the possibility of launching a new instance at the same time. This instance is a deterministic run, which uses a different driver than the training version.
The multi instance option consists of providing automatic network segmentation to ROS2 and Gazebo, which allows to launch multiple simultaneous instances.
Quick demonstration of a converged policy using ROS2Learn framework and the gym-gazebo2 toolkit. We execute a deterministic run and also use settings that replicate a real behavior of the robot.
Start training and visualize the simulation without going through the step by step installation process. In this video we execute a simple test example and visualize it from our main OS. Gazebo must be already installed there, Ubuntu 18 in our case.