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Doosan robotic arm, simulation, control, visualization in Gazebo and ROS2 for Reinforcement Learning.

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Robotic Arm Simulation in ROS2 and Gazebo

Note:

I will take this project again very soon; the idea is to use state-of-the-art RL algorithms. Also, move to the new gazebo and the latest ROS version. Please let me know if you want to collaborate.

General Overview

This repository includes: First, how to simulate a 6DoF Robotic Arm from scratch using GAZEBO and ROS2. Second, it provides a custom Reinforcement Learning Environment where you can test the Robotic Arm with your RL algorithms. Finally, we test the simulation and environment with a reacher target task, using RL and the 6DoF Robotic Arm with a visual target point.

Prerequisites

Library Version (TESTED)
Ubuntu 20.04
ROS2 Foxy link
ros2_control link
gazebo_ros2_control link

How to run this Repository

In the following links you can find a step-by-step instruction section to run this repository and simulate the robotic arm:

  • Simulation in Gazebo and ROS2 --> Tutorial-link

    • Configurate and spawn the robotic arm in Gazebo.
    • Move the robot with a simple position controller.
  • Custom RL Environment --> Tutorial-link

    • A complete Reinforcement Learning environment simulation.
  • Reacher task with RL --> Cooming soon

    • Robot reacher task.

Citation

If you use either the code, data or the step from the tutorial-blog in your paper or project, please kindly star this repo and cite our webpage

Acknowledgement

I want to thank Doosan Robotics for their repositories, and packages where they took part of this code.

Also, thanks to the authors of these repositories and their tutorials where I took some ideas

Contact

Please feel free to contact me or open an issue if you have questions or need additional explanations.

The released codes are only allowed for non-commercial use.

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Doosan robotic arm, simulation, control, visualization in Gazebo and ROS2 for Reinforcement Learning.

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