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

Published in

arXiv.org(2019)

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TL;DR

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 is presented.

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.

Authors

N. G. Lopez

1 papers

Y. Nuin

1 papers

Elias Barba Moral

1 papers

References26 items

ROS2Learn: a reinforcement learning framework for ROS 2

Sequential Learning of Movement Prediction in Dynamic Environments using LSTM Autoencoder

Danger-aware Weighted Advantage Composition of Deep Reinforcement Learning for Robot Navigation

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

Learning dexterous in-hand manipulation

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

Published in

arXiv.org(2019)

External Links:

Generate Graph

TL;DR

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 is presented.

Abstract

Authors

N. G. Lopez

1 papers

Y. Nuin

1 papers

Elias Barba Moral

1 papers

References26 items

ROS2Learn: a reinforcement learning framework for ROS 2

Sequential Learning of Movement Prediction in Dynamic Environments using LSTM Autoencoder

Danger-aware Weighted Advantage Composition of Deep Reinforcement Learning for Robot Navigation

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

Learning dexterous in-hand manipulation

Lander Usategui San Juan

2 papers

Deep Learning Based on Smooth Driving for Autonomous Navigation

Towards self-adaptable robots: from programming to training machines

Hierarchical Learning for Modular Robots

Asynchronous deep reinforcement learning for the mobile robot navigation with supervised auxiliary tasks

Sim-to-Real Transfer of Robotic Control with Dynamics Randomization

Deep Reinforcement Learning: A Brief Survey

Proximal Policy Optimization Algorithms

Transferring End-to-End Visuomotor Control from Simulation to Real World for a Multi-Stage Task

Domain randomization for transferring deep neural networks from simulation to the real world

Transfer from Simulation to Real World through Learning Deep Inverse Dynamics Model

Extending the OpenAI Gym for robotics: a toolkit for reinforcement learning using ROS and Gazebo

An Empirical Exploration of Recurrent Network Architectures

A Critical Review of Recurrent Neural Networks for Sequence Learning

MuJoCo: A physics engine for model-based control

Design and use paradigms for Gazebo, an open-source multi-robot simulator

Teaching Ground Vehicles to Navigate Autonomously with Reinforcement Learning

Path Planning Amidst Moving Obstacles

A brief survey of deep reinforcement learning

ROS: an open-source Robot Operating System

“Learning continuous control through proximal policy optimization for mobile robot navigation.”

Field of Study

Computer ScienceEngineering

Journal Information

Name

ArXiv

Volume

abs/2005.00687

Venue Information

Name

arXiv.org

Type

URL

https://arxiv.org

Alternate Names

ArXiv