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

arXiv: https://arxiv.org/abs/1903.06278 · HTML: https://mlsanity.com/paper/1903.06278/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2019.

## Built on by (most cited first, 7 of 7)

- [A Review of Nine Physics Engines for Reinforcement Learning Research](https://mlsanity.com/paper/2407.08590/) (2024)
- [MarsExplorer: Exploration of Unknown Terrains via Deep Reinforcement Learning and Procedurally Generated Environments](https://mlsanity.com/paper/2107.09996/) (2021)
- [PIC4rl-gym: a ROS2 modular framework for Robots Autonomous Navigation with Deep Reinforcement Learning](https://mlsanity.com/paper/2211.10714/) (2022)
- [EAGERx: Graph-Based Framework for Sim2real Robot Learning](https://mlsanity.com/paper/2407.04328/) (2024)
- [A ROS 2 Wrapper for Florence-2: Multi-Mode Local Vision-Language Inference for Robotic Systems](https://mlsanity.com/paper/2604.01179/) (2026)
- [RouteFormer: A Transformer-Based Routing Framework for Autonomous Vehicles](https://mlsanity.com/paper/2504.05407/) (2025)
- [ARMATA: Auto-Regressive Multi-Agent Task Assignment](https://mlsanity.com/paper/2605.04225/) (2026)

## Abstract (arXiv)

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.