Introducing robo-gym: An Open Source Toolkit for Distributed Deep Reinforcement Learning on Real and Simulated Robots

ROS ROS General
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Dear ROS Community,

Together with my colleagues, I am happy to introduce to you robo-gym: an open source toolkit for distributed reinforcement learning on real and simulated robots.

robo-gym provides a collection of reinforcement learning environments involving robotic tasks applicable in both simulation and real world robotics. Additionally, we provide the tools to facilitate the creation of new environments featuring different robots and sensors.

Main Features

  • OpenAI Gym interface for all the the environments
  • simulated and real robots interchangeability, which enables a seamless transfer from training in simulation to application on the real robot.
  • built-in distributed capabilities, which enable the use of distributed algorithms and distributed hardware
  • based only on open source software, which allows to develop applications on own hardware and without incurring in cloud services fees or software licensing costs
  • integration of 2 commercially available industrial robots: MiR 100, UR 10 (more to come)
  • the provided tasks have been successfully solved both in simulation and on the real robot using a DRL algorithm trained exclusively in the simulation environments

robo-gym simulated environments are built with Gazebo and use ROS controllers making it easy to expand the library of RL Environments with new robots and sensors.

Getting Started

You can find all the documentation directly in the GitHub repo, it should be quite easy to start and play around with the already existing environments.

We also included some basic information on how to integrate new environments, but if you are interested in integrating you own robot, sensor or task please reach out, we would be happy to support you with that!

How to contact us

If you encounter any issues with robo-gym the best way to contact us is to directly open a new issue on GitHub.

If you are interested in expanding the framework or start a collaboration please drop us an email at

Matteo Lucchi
matteo.lucchi@joanneum.at

Friedemann Zindler
friedemann.zindler@joanneum.at

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