A fast and modular MPPI implementation with Halton spline sampling

Credits

Much of the backbone of the mppi implementation is based on pytorch_mppi, however it has been modified with addition sampling modes (e.g. Halton spline sampling) and support for ancillary controllers (priors) by Corrado Pezzato, Elia Trevisan and Chadi Salmi.

Structure

The project is structured as follows:

• examples/: Contains the point robot example.
• mppi_torch/: Contains the implementation of the MPPI algorithm and its utilities.
• pyproject.toml and poetry.lock: Configuration files for project dependencies.

Installation

To install the project, follow these steps:

# Clone the repository
git clone <repository-url>

# Navigate to the project directory
cd <project-directory>

# Install dependencies
poetry install

Usage

To run the point robot example:

poetry shell
cd examples/point_robot
python run.py

Contributing

Contributions are welcome. Please submit a pull request.

Cite

This repository was originally developed for our paper Sampling-based Model Predictive Control Leveraging Parallelizable Physics Simulations. You can find that code here. If relevant, consider citing:

@misc{pezzato2023samplingbased,
      title={Sampling-based Model Predictive Control Leveraging Parallelizable Physics Simulations}, 
      author={Corrado Pezzato and Chadi Salmi and Max Spahn and Elia Trevisan and Javier Alonso-Mora and Carlos Hernandez Corbato},
      year={2023},
      eprint={2307.09105},
      archivePrefix={arXiv},
      primaryClass={cs.RO}
}

We also recently added the possibility of biasing the sampling distribution with ancillary controllers (or priors), as in Biased-MPPI: Informing Sampling-Based Model Predictive Control by Fusing Ancillary Controllers. Examples of how to use prior controllers with this repository can be found here. If you use this feature, consider citing:

@ARTICLE{trevisan2024biased,
  author={Trevisan, Elia and Alonso-Mora, Javier},
  journal={IEEE Robotics and Automation Letters}, 
  title={Biased-MPPI: Informing Sampling-Based Model Predictive Control by Fusing Ancillary Controllers}, 
  year={2024},
  volume={9},
  number={6},
  pages={5871-5878},
  keywords={Costs;Planning;Monte Carlo methods;Mathematical models;Optimal control;Vehicle dynamics;Trajectory;Motion and path planning;optimization and optimal control;collision avoidance;sampling-based MPC;MPPI},
  doi={10.1109/LRA.2024.3397083}}