gym-gazebo2, a toolkit for reinforcement learning using ROS 2 and Gazebo

TL;DR

This paper introduces gym-gazebo2, an upgraded ROS 2 and Gazebo-based reinforcement learning toolkit compatible with OpenAI Gym, featuring a benchmarking system for robotics and demonstrating its effectiveness with modular robotic arms.

Contribution

The paper presents a new ROS 2 architecture for gym-gazebo, enabling benchmarking and evaluation of RL algorithms on complex robotic environments.

Findings

Achieved millimeter-scale accuracy in robotic arm environments.

Demonstrated the feasibility of the toolkit for industrial robotics.

Provided a platform for comparing different RL techniques.

Abstract

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.