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Overview

This repo contains a model predictive controller based on the single rigid body model and written in Python. It cames in two flavours: gradient-based via acados or sampling-based via jax. The controller is tested on real robots and is compatible with Mujoco. See the end of this README if you want to cite this work.

Features gradient-based mpc:

  • less than 5ms computation on an intel i7-13700H cpu
  • optional integrators for model mismatch
  • optional smoothing for the ground reaction forces
  • optional foothold optimization
  • optional real-time iteration or advanced-step real-time iteration
  • optional zero moment point/center of mass constraints
  • optional lyapunov-based criteria

Features sampling-based mpc:

  • 10000 parallel rollouts in less than 2ms on an nvidia 4050 mobile gpu!
  • optional step frequency adaptation for enhancing robustness
  • implements different strategies: random sampling, mppi, or cemppi
  • different control parametrizations: zero-order, linear splines or cubic splines (see mujoco-mpc)
Experiments with Aliengo
Simulations with Go2

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.

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 ..
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 inside the folder gym-quadruped and install it:

    pip install -e .

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

    pip install -e .

How to run

  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

Citing this work

If you find the work useful, please consider citing our work:

@INPROCEEDINGS{turrisi2024sampling,
  title={On the Benefits of GPU Sample-Based Stochastic Predictive Controllers for Legged Locomotion},
  author={Turrisi, Giulio and Modugno, Valerio and Amatucci, Lorenzo and Kanoulas, Dimitrios and Semini, Claudio},
  booktitle={2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  year={2024}
}

Maintainer

This repository is maintained by Giulio Turrisi and Daniel Ordonez.