Bogdan Vlahov, Jason Gibson, Manan Gandhi, Evangelos A. Theodorou
{"title":"MPPI-Generic:用于随机优化的 CUDA 库","authors":"Bogdan Vlahov, Jason Gibson, Manan Gandhi, Evangelos A. Theodorou","doi":"arxiv-2409.07563","DOIUrl":null,"url":null,"abstract":"This paper introduces a new C++/CUDA library for GPU-accelerated stochastic\noptimization called MPPI-Generic. It provides implementations of Model\nPredictive Path Integral control, Tube-Model Predictive Path Integral Control,\nand Robust Model Predictive Path Integral Control, and allows for these\nalgorithms to be used across many pre-existing dynamics models and cost\nfunctions. Furthermore, researchers can create their own dynamics models or\ncost functions following our API definitions without needing to change the\nactual Model Predictive Path Integral Control code. Finally, we compare\ncomputational performance to other popular implementations of Model Predictive\nPath Integral Control over a variety of GPUs to show the real-time capabilities\nour library can allow for. Library code can be found at:\nhttps://acdslab.github.io/mppi-generic-website/ .","PeriodicalId":501175,"journal":{"name":"arXiv - EE - Systems and Control","volume":"75 2 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"MPPI-Generic: A CUDA Library for Stochastic Optimization\",\"authors\":\"Bogdan Vlahov, Jason Gibson, Manan Gandhi, Evangelos A. Theodorou\",\"doi\":\"arxiv-2409.07563\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper introduces a new C++/CUDA library for GPU-accelerated stochastic\\noptimization called MPPI-Generic. It provides implementations of Model\\nPredictive Path Integral control, Tube-Model Predictive Path Integral Control,\\nand Robust Model Predictive Path Integral Control, and allows for these\\nalgorithms to be used across many pre-existing dynamics models and cost\\nfunctions. Furthermore, researchers can create their own dynamics models or\\ncost functions following our API definitions without needing to change the\\nactual Model Predictive Path Integral Control code. Finally, we compare\\ncomputational performance to other popular implementations of Model Predictive\\nPath Integral Control over a variety of GPUs to show the real-time capabilities\\nour library can allow for. Library code can be found at:\\nhttps://acdslab.github.io/mppi-generic-website/ .\",\"PeriodicalId\":501175,\"journal\":{\"name\":\"arXiv - EE - Systems and Control\",\"volume\":\"75 2 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - EE - Systems and Control\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2409.07563\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - EE - Systems and Control","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.07563","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
MPPI-Generic: A CUDA Library for Stochastic Optimization
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/ .