This paper presents an upgraded, real world application oriented version of gym-gazebo, the Robot Operating System (ROS) and Gazebo based Reinforcement Learning (RL) toolkit, which complies with OpenAI Gym. The content discusses the new ROS 2 based software architecture and summarizes the results obtained using Proximal Policy Optimization (PPO). Ultimately, the output of this work presents a benchmarking system for robotics that allows different techniques and algorithms to be compared using the same virtual conditions. We have evaluated environments with different levels of complexity of the Modular Articulated Robotic Arm (MARA), reaching accuracies in the millimeter scale. The converged results show the feasibility and usefulness of the gym-gazebo 2 toolkit, its potential and applicability in industrial use cases, using modular robots.
@article{arxiv.1903.06278,
title = {gym-gazebo2, a toolkit for reinforcement learning using ROS 2 and Gazebo},
author = {Nestor Gonzalez Lopez and Yue Leire Erro Nuin and Elias Barba Moral and Lander Usategui San Juan and Alejandro Solano Rueda and Víctor Mayoral Vilches and Risto Kojcev},
journal= {arXiv preprint arXiv:1903.06278},
year = {2019}
}