There are 2 repositories under mppi topic.
PythonLinearNonLinearControl is a library implementing the linear and nonlinear control theories in python.
Model Predictive Path Integral (MPPI) with approximate dynamics implemented in pytorch
Python implementation of MPPI (Model Predictive Path-Integral) controller to understand the basic idea. Mandatory dependencies are numpy and matplotlib only.
A GPU implementation of Model Predictive Path Integral (MPPI) control that uses a probabilistic traversability model for planning risk-aware trajectories.
25 path-tracking algorithms are (goint to be) implemented with python.
[ICRA2024] Stein Variational Guided Model Predictive Path Integral Control: Proposal and Experiments with Fast Maneuvering Vehicles
Model Predictive Path Integral Control (MPPI) with PyTorch
Colab notebooks showcasing experiments on MPPI (model predictive path integral control) and CBF (control barrier function). Utilizes jax to accelerate computation.
off-road navigation simulator for benchmarking planning algorithms
SIA - C++/Python library for model-based stochastic estimation and optimal control
A simple and extensible Octave/Matlab library for Model Predictive Path Integral control scheme.
A gym-like classical control benchmark for evaluating the robustnesses of control and reinforcement learning algorithms.
Bayesian Optimization for MPPI Control of Robot Arm Planar Pushing
Optimal Control via Combined Inference and Numerical Optimization
Basic tutorials and examples of using numba for GPU-based computation.
Model based RL using Model Predictive Control
Python scripts to train a neuralnet for MPPI
Simple implementation of MPPI in Matlab
Topics include function approximation, learning dynamics, using learned dynamics in control and planning, handling uncertainty in learned models, learning from demonstration, and model-based and model-free reinforcement learning.
C++ Implementation of MPPI-IPDDP (Model Predictive Path Integral - Interior Point Differential Dynamic Programming) and Testing with MPPI variants and Multiple Environments