Year: 2024
Type: preprint
Abstract: This paper introduces a new C++/CUDA library for GPU-accelerated stochastic optimization called MPPI-Generic. It provides implementations of Model Predictive Path Integral control, Tube-Model Predictive Path Integral Control, and Robust Model Predictive Path Integral Control, and allows for these algorithms to be used across many pre-existing dynamics models and cost functions. Furthermore, researchers can create their own dynamics models or cost functions following our API definitions without needing to change the actual Model Predictive Path Integral Control code. Finally, we compare computational performance to other popular implementations of Model Predictive Path Integral Control over a variety of GPUs to show the real-time capabilities our library can allow for. Library code can be found at: https://acdslab.github.io/mppi-generic-website/ .
Source: arXiv (Cornell University)
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Funders: U.S. Department of Defense, U.S. Department of Energy, National Aeronautics and Space Administration, California Institute of Technology, Defense Advanced Research Projects Agency, National Nuclear Security Administration, Jet Propulsion Laboratory, Langley Research Center, Advanced Research Projects Agency, Office of Naval Research, Sandia National Laboratories
OpenAlex ID: W4403663073