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README

pyCub is iCub humanoid robot simulator written in Python. It uses PyBullet for simulation and Open3D for visualization.

Known bugs

Installation

  1. Install dependencies
    • use Docker
    • or install python3 (3.8 and higher; tested in 3.11) and python3 -m pip install pybullet numpy scipy open3d
      • open3d 0.16.0 and higher is required
        • you may need to upgrade pip first: python3 -m pip install --upgrade pip
  2. Pull this repository
  3. try run some example, e.g., python3 PATH_TO_THE_REPOSITORY/icub_pybullet/examples/push_the_ball_cartesian.py
  4. (optional, but recommended) add export PYTHONPATH=$PYTHONPATH:PATH_TO_THE_REPOSITORY/icub_pybullet to your ~/.bashrc file
    • this will allow you to run scripts from any location

Examples

  • push_the_ball_pure_joints.py contains an example that shows how to control the robot in joint space
  • push_the_ball_cartesian.py contains an example that shows how to control the robot in Cartesian space
  • skin_test.py contains an example with balls falling the robot and skin should turn green on the places where contact occurs. You may want to slow the simulation a little bit to see that :)

Information

  • documentation can be found at lukasrustler.cz/pycub or in pyCub.pdf
  • presentation with description of functionality can be found at pyCub presentation
  • simulator code is in pycub.py
    • it uses PyBullet for simulation and provides high-level interface
  • visualization code in visualizer.py
    • it uses Open3D for visualization as it is much more customizable than PyBullet default GUI
  • movement is done using position control. You can either use position control directly (pycub.move_position()) or use cartesian control (pycub.move_cartesian())
    • Neither of these check for collision before movement!
    • Function pycub.motion_done() check whether all joints reached the target or whether collision ocurred. If collision, the variable pycub.collision_during_movement is set. You can also run pycub.motion_done() with check_collision=False to ignore collision checks, e.g., to get out of collision state
  • until you add export PYTHONPATH=$PYTHONPATH:PATH_TO_THE_REPOSITORY/icub_pybullet to your ~/.bashrc file (or change PYTHONPATH everytime you open a new terminal) you have to add something like sys.path.append(0, "PATH_TO_THE_REPOSITORY/icub_pybullet") to every file you want to run
    • scripts in examples folder already contain such line, so the examples can be run easily

Docker

https://github.com/rustlluk/easy-docker is utilized to use Docker

Installation

  • install docker-engine (DO NOT INSTALL DOCKER DESKTOP), do post-installation steps and (optional) install nvidia-docker for GPU support

  • For ubuntu (and Mint, but you have) users:

    • if you are a mint user, change VERSION_CODENAME to UBUNTU_CODENAME
    sudo apt-get update
sudo apt-get install ca-certificates curl gnupg
sudo install -m 0755 -d /etc/apt/keyrings
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /etc/apt/keyrings/docker.gpg
sudo chmod a+r /etc/apt/keyrings/docker.gpg

echo \
  "deb [arch="$(dpkg --print-architecture)" signed-by=/etc/apt/keyrings/docker.gpg] https://download.docker.com/linux/ubuntu \
  "$(. /etc/os-release && echo "$VERSION_CODENAME")" stable" | \
  sudo tee /etc/apt/sources.list.d/docker.list > /dev/null
sudo apt-get update
sudo apt-get install docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin
    • and post-installation to use docker without sudo:
    sudo groupadd docker
sudo usermod -aG docker $USER
    • and restart your computer

  • clone this repository

    cd SOME_PATH
git clone https://github.com/rustlluk/pyCub.git

  • (optionally) rename it to be called the same as in docker

    mv PATH_TO_THE_REPOSITORY/pycub SOME_PATH/pycub_ws

  • build the docker (see Parameters for more parameters)

    cd SOME_PATH/pycub_ws/Docker
./deploy.py -b -p PATH_TO_THE_REPOSITORY/pycub_ws -c pycub

  • after you built the container, you can run it next time as

    ./deploy.py -e -c pycub

  • if you want to open new terminal in existing container, run

    ./deploy.py -c pycub -t

Docker + PyCharm

You have two option:

  1. Either run pycharm from docker
  2. Open your pycharm on your host machine:
    • add ssh interpreter
      • user docker
      • ip can be localhost or ip where you run the docker
      • port 2222
    • uncheck automatic upload to remote folder
    • change remote path to /home/docker/pycub_ws

Common steps:

  • mark all folder icub_pybullet as Source Root
  • for X11 forwarding:
    • Click on configurations drop menu -> Edit Configurations -> Edit configuration templates -> Python -> Edit environment variables -> add DISPLAY with the same value as in docker and uncheck 'Include system environment variables'. Every new configuration will have that settings from now
      • if you already have configuration created before doing the above -> delete it and create again, or change it manually

Deploy Parameters

  • cd to folder with Dockerfile
  • ./deploy.py
    • -b or --build when building
      • default: False
    • -nv or --nvidia when you want to use your Nvidia card
      • you have to use it when creating a new container
      • default: False
    • -e if you just want to run existing docker without building
      • default: False
    • -p or --path with path to current folder
      • default: ""
    • c or --container with desired name of the new, created container
      • default: my_new_docker
    • t or --terminal to run new terminal in running docker session
      • default: False
    • -pv or --python-version to specify addition python version to install
      • default: 3.11
    • -pcv or --pycharm-version to specify version of pycharm to use
      • default: 2023.2.3
    • -bi or --base-image to specify base image that will be used
      • default: nvidia/cuda:11.0.3-devel-ubuntu20.04
      • other can be found at hub.docker.com

Do this on computer where you will run the code. If you have a server you have to run it on the server over SSH to make things work properly.

FAQ

  • applications do not run on your screen (or you have some strange screen related errors)
    • in another terminal run xhost local:docker
      • if it does not work, try xhost +
        • if this does not work, nothing can be done
  • you get error of not being in sudo group when running image
    • check output of id -u command. If the output is not 1000 you have to build the image by yourself and can not pull it
      • this happens when your account is not the first one created on your computer
  • sudo apt install something does not work
    • you need to run sudo apt update first after you run the container for the first time
      • apt things are removed in Dockerfile, so it does not take unnecessary space in the image