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Doosan robotic arm, simulation, control, visualization in Gazebo and ROS2 for Reinforcement Learning.
| Date | Stars |
|---|---|
| 2026-07-24 | 352 |
| 2026-07-25 | 352 |
| 2026-07-28 | 352 |
| 2026-07-30 | 352 |
| 2026-07-31 | 355 |
| 2026-08-04 | 354 |
| 2026-08-06 | 354 |
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<h1 align="center"> <br> Robotic Arm Simulation in ROS 2 and Gazebo <br> </h1> > ## 🚀 Migrated to ROS 2 Jazzy + Gazebo Harmonic — June 2026 > > This repository has been **fully migrated from ROS 2 Foxy + Gazebo Classic > (both end-of-life) to ROS 2 Jazzy + Gazebo Harmonic on Ubuntu 24.04.** > > The arm, the target sphere, and the full RL environment all run on the new > stack. Everything below targets Jazzy + Harmonic. If you need the old > Foxy / Gazebo Classic version, check the git history before this update. ## General Overview This repository shows, **from scratch**, how to: 1. Simulate a 6-DoF robotic arm (Doosan a0912 / m1013) in **Gazebo** and **ROS 2**. 2. Use a custom **Reinforcement Learning environment** to test the arm with your own RL algorithms. 3. Run a **reacher task**: the arm reaches a visual target (green sphere) that resets to a new random position each episode. <p align="center"> <img src="https://github.com/dvalenciar/robotic_arm_environment/blob/main/images/doosan.gif" alt="Doosan arm reacher demo"> </p> ## Prerequisites You only need the platform installed first: | Requirement | Version (tested) | |---|---| | Ubuntu | 24.04 | | ROS 2 | Jazzy — [install](https://docs.ros.org/en/jazzy/Installation.html) | ## Dependencies The `apt` command below installs everything else this repo needs — **Gazebo Harmonic** (pulled in by `ros-gz`), the `ros_gz` bridge/sim, `ros2_control`, `gz_ros2_control`, and the controllers. Safe to run on any ROS 2 Jazzy install; packages you already have are simply skipped. ```bash sudo apt install ros-jazzy-ros-gz ros-jazzy-gz-ros2-control \ ros-jazzy-ros2-control ros-jazzy-ros2-controllers \ ros-jazzy-joint-state-publisher-gui ros-jazzy-xacro \ ros-jazzy-robot-state-publisher ros-jazzy-rviz2 ros-jazzy-tf2-ros ``` ## Installation Create a colcon workspace and clone this repository into its `src` folder: ```bash mkdir -p ~/ros2_ws/src cd ~/ros2_ws/src git clone https://github.com/dvalenciar/robotic_arm_environment.git cd ~/ros2_ws ``` ## Build ```bash source /opt/ros/jazzy/setup.bash cd ~/ros2_ws colcon build source install/setup.bash ``` ## Quick start ```bash # Full RL environment: arm + target sphere in Gazebo ros2 launch my_environment_pkg my_environment.launch.py # In a second terminal — run a few random-action episodes ros2 run my_environment_pkg run_environment ``` Test pieces individually: ```bash # Arm only (spawn + joint_trajectory_controller) ros2 launch my_doosan_pkg my_doosan_gazebo_controller.launch.py # Arm in RViz with joint sliders ros2 launch my_doosan_pkg my_doosan_rviz.launch.py # Target sphere only (standalone) ros2 launch my_sphere_pkg my_sphere_standalone.launch.py ros2 run my_sphere_pkg my_client_node # teleport it to a random pose ``` ## Packages | Package | Role | |---|---| | `my_doosan_pkg` | Robot description (xacro), `gz_ros2_control`, worlds, controllers | | `my_sphere_pkg` | Target sphere: spawn, pose readback, and reset via `ros_gz` | | `my_environment_pkg` | RL environment node tying the arm + sphere together | ## Citation If the code helps your work, please star this repo. ## Acknowledgement Thanks to Doosan Robotics for their repositories and packages: * https://github.com/doosan-robotics/doosan-robot2 * https://github.com/doosan-robotics/doosan-robot * https://www.doosanrobotics.com/en/ And to the authors of these repositories and tutorials for the ideas: * https://github.com/noshluk2/ROS2-Ultimate-learners-Repository/tree/main/bazu * https://github.com/TomasMerva/ROS_KUKA_env ## Contact Please open an issue if you have questions or need additional explanations. ###### The released code is only allowed for non-commercial use.
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:7452f47c402be1d0, topic:reinforcement-learning, desc:reinforcement learning, readme:reinforcement learning
matched fp:7452f47c402be1d0, topic:ros2, readme:robotics