User Guide

Robotics Academy supports Linux (Ubuntu 18.04, 20.04, 22.04 and other distributions), MacOS and Windows.

1. Installation

The installation of ROS, Gazebo, etc. has been greatly simplified, as all the required dependencies are already pre-installed in the Robotics-Academy Docker Image (RADI). The users should:

  1. Download Docker. Windows users should choose WSL 2 backend Docker installation if possible, as it has better performance than Hyper-V.

  2. Pull the current distribution of Robotics Academy Docker Image:

      docker pull jderobot/robotics-academy:latest

  • In order to obtain optimal performance, Docker should be using multiple CPU cores. In case of Docker for Mac or Docker for Windows, the VM should be assigned a greater number of cores.

  • It is recommended to use the latest image. However, older distributions of RADI (Robotics-Academy Docker Image) can be found here.

2. How to use and perform the exercises?

  1. Start a new docker container of the image and keep it running in the background:

      docker run --rm -it -p 7164:7164 -p 2303:2303 -p 1905:1905 -p 8765:8765 -p 6080:6080 -p 1108:1108 -p 7163:7163 jderobot/robotics-academy

  2. On the local machine navigate to 127.0.0.1:7164/ in the browser and choose the desired exercise.

  3. Wait for the Connect button to turn green and display “Connected”. Click on the “Launch” button and wait for some time until an alert appears with the message Connection Established and button displays “Ready”.

  4. The exercise can be used, and you may start programming your solution, after the alert.

Enable GPU Acceleration

ROS and Gazebo can be accelerated within RoboticsAcademy thanks to VirtualGL if a GPU is available.

Linux

  • Intel: For Linux machines and Intel GPUs, acceleration can be achieved by simply setting the --device argument when running the Docker container: 
    docker run --rm -it --device /dev/dri -p 7164:7164 -p 2303:2303 -p 1905:1905 -p 8765:8765 -p 6080:6080 -p 1108:1108 -p 7163:7163 jderobot/robotics-academy
  • NVIDIA: For NVIDIA GPUs, acceleration can be achieved by installing the nvidia-container-runtime package, and then running the command above, but adding the --gpus all flag: 
    docker run --rm -it --gpus all --device /dev/dri -p 7164:7164 -p 2303:2303 -p 1905:1905 -p 8765:8765 -p 6080:6080 -p 1108:1108 -p 7163:7163 jderobot/robotics-academy
  • MULTIPLE GPUs: If the PC has several GPUs, we can choose which one will be used by setting the DRI_NAME environment variable (e.g. card0 or card1) 
    docker run --rm -it --gpus all --device /dev/dri -e DRI_NAME=card1 -p 7164:7164 -p 2303:2303 -p 1905:1905 -p 8765:8765 -p 6080:6080 -p 1108:1108 -p 7163:7163 jderobot/robotics-academy

    You can check the names associated with your graphic cards by running:

     drm-info -j | jq 'with_entries(.value |= .driver.desc)'

    You should get something like:

    {
"/dev/dri/card1": "NVIDIA DRM driver",
"/dev/dri/card0": "Intel Graphics"
}

Windows

For Windows machines, GPU acceleration to Docker is an experimental approach and can be implemented as per instructions given here.