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Rather than programming, training allows robots to achieve behaviors thatgeneralize better and are capable to respond to real-world needs. However, suchtraining requires a big amount of experimentation which is not always feasiblefor a physical robot. In this work, we present robot_gym, a framework toaccelerate robot training through simulation in the cloud that makes use ofroboticists' tools, simplifying the development and deployment processes onreal robots. We unveil that, for simple tasks, simple 3DoF robots require morethan 140 attempts to learn. For more complex, 6DoF robots, the number ofattempts increases to more than 900 for the same task. We demonstrate that ourframework, for simple tasks, accelerates the robot training time by more than33% while maintaining similar levels of accuracy and repeatability.

Robot_gym: accelerated robot training through simulation in the cloud with ROS and Gazebo