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Dependencies

Gradient-based MPC: It uses CasADI to define the model and acados to solve the optimal control problem. Sampling-based MPC: jax for both. The simulation environment is based on Mujoco.

Conda installation

  1. install miniforge (x86_64 or arm64 depending on your platform)

  2. create an environment using the file in the folder installation choosing between nvidia and integrated gpu, either with or without ros (to run the simulation, you don't need ros!):

    conda env create -f mamba_environment.yml

  3. clone the other submodules:

    git submodule update --init --recursive

  4. activate the conda environment

    conda activate quadruped_pympc_env

  5. go inside the folder acados and compile it pressing:

    mkdir build
cd build
cmake -DACADOS_WITH_SYSTEM_BLASFEO:BOOL=ON -DCMAKE_POLICY_VERSION_MINIMUM=3.5 ..
make install -j4
pip install -e ./../interfaces/acados_template

  6. inside the file .bashrc, given your path_to_acados, put:

    export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:"/path_to_acados/lib"
export ACADOS_SOURCE_DIR="/path_to_acados"

    Notice that if you are using Mac, you should modify the file .zshrc adding

    export DYLD_LIBRARY_PATH=$LD_LIBRARY_PATH:"/path_to_acados/lib"
export ACADOS_SOURCE_DIR="/path_to_acados"

The first time you run the simulation with acados, in the terminal you will be asked to install tera_render. You should accept to proceed.

  1. go to Quadruped-PyMPC initial folder and install it:

    pip install -e .

How to run - Simulation

  1. activate the conda environment

    conda activate quadruped_pympc_env

  2. go in the main Quadruped-PyMPC folder and press

    python3 simulation/simulation.py

In the file config.py, you can set up the robot, the mpc type (gradient, sampling..), its proprierties (real time iteration, sampling type, foothold optimization..), and other simulation params (reference, gait type..).

  1. you can interact with the simulation with your mouse to add disturbances, or with the keyboard by pressing
arrow up, arrow down -> add positive or negative forward velocity
arrow left, arrow right -> add positive or negative yaw velocity
ctrl -> set zero all velocities

How to run - ROS2

During the installation procedure, use the file mamba_environment_ros2.yml. Then:

  1. activate the conda environment

    conda activate quadruped_pympc_ros2_env

  2. go in the folder ros2/msgs_ws and compile the messages

    colcon build
source install/setup.bash

  3. you can run now the script

    python3 ros2/run_controller.py

  4. if you want to test the above node with a simulator, for example to test ros2 delay, you can run

    python3 ros2/run_simulator.py

  5. joystick

    ros2 launch teleop_twist_joy teleop-launch.py joy_config:='xbox'

  6. general commands from terminal available here

  7. for a real-robot deployment, use a nice state estimator

Docker Installation

Warning

Currently, the Docker installation only supports Integrated GPUs and the ROS 2 Humble environment. CUDA integration and support for other environments are under development (WIP).

  1. Clone the repository - Ensure you download all submodules during cloning:
git clone --recursive https://github.com/iit-DLSLab/Quadruped-PyMPC.git
cd Quadruped-PyMPC
  1. Build the image - Build the environment using Docker Compose:
docker compose build

How to Run - Simulation

  1. Deploy the container - Start the container in detached mode:
docker compose up -d
  1. Access the container - Navigate to the project folder and shell into the running container:
docker compose exec pympc bash

Note

  • Byobu/Tmux: We recommend using Byobu (already installed) for a better experience.
  • Conda: Every new shell session inside the container automatically activates the required Conda environment.
  1. Run the simulation - Execute the following scripts in their respective terminals:
python3 simulation/simulation.py
  1. Clean up - To stop and remove the containers, run this command in your host terminal:
docker compose down

How to run - ROS2

  1. Deploy the container - Start the container in detached mode:
docker compose up -d
  1. Access the container - Navigate to the project folder and shell into the running container:
docker compose exec pympc bash
  1. Run the controller - Execute the following scripts in their respective terminals:
  • Terminal 1 (Controller):
python ros/run_controller.py
  1. Access the container - open a second terminal. This if you want to test the above node with a simulator, for example, to test ros2 delay, you can run
  • Terminal 2 (Simulator):
python ros/run_simulator.py
  1. Clean up - To stop and remove the containers, run this command in your host terminal:
docker compose down