A generic sampling-based MPC python library based on Jax.
Implements the Feedback-MPPI method presented in the related paper to compute a first order approximation of the MPPI solution suitable for high-frequency state feedback corrections.
@article{belvedere2025feedbackmppi,
title={Feedback-MPPI: Fast Sampling-Based MPC via Rollout Differentiation -- Adios low-level controllers},
author={Tommaso Belvedere and Michael Ziegltrum and Giulio Turrisi and Valerio Modugno},
year={2025},
url={https://arxiv.org/abs/2506.14855},
}- Nvidia cuda toolkit installed system-wide (if you want to use a local CUDA version)
- miniforge
Create the conda environment with
mamba env create -f environment.yml
Activate the environment with
conda activate sbmpc
Depending on the CUDA settings of your machine, choose between
- CPU-only acceleration
pip install -e .
- GPU acceleration with pip-installed CUDA libraries
pip install -e ".[cuda12]"
- GPU acceleration with locally installed CUDA
pip install -e ".[cuda12_local]"
Refer to the Jax documentation for details.
- Tommaso Belvedere, CNRS (core developer, project lead)
- Michael Ziegltrum, UCL (feature developer)
- Chidinma Ezeji, UCL (feature developer)
- Giulio Turrisi, IIT (project lead)
- Valerio Modugno, UCL (core developer, project lead)