MPPI-Generic: A CUDA Library for Stochastic Optimization

2024-09-11

arXiv.org

AI Summary

This work addresses the low reusability and insufficient GPU acceleration support of existing stochastic optimal control algorithms. We propose the first modular, plug-and-play open-source CUDA library for real-time stochastic model predictive control. The library decouples controller cores—including MPPI, Tube-MPPI, and Robust MPPI—from user-defined dynamics models and cost functions, enabling seamless integration of custom components via a unified C++/CUDA API. Built upon a tight fusion of model predictive control and path integral optimal control theory, it achieves millisecond-level closed-loop execution on multiple generations of NVIDIA GPUs. Experimental evaluation demonstrates 10–50× speedup over state-of-the-art CPU- and GPU-based implementations, while significantly improving cross-task transferability and development efficiency. The library establishes a general-purpose, high-performance infrastructure for real-time stochastic optimization control of dynamic systems.

Technology Category

• Reasoning under Uncertainty: Stochastic Optimization
• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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/ .

Problem

• Introduces a GPU-accelerated library for stochastic trajectory optimization.
• Enables use of various dynamics models and cost functions without code modification.
• Demonstrates real-time capabilities through performance comparisons on multiple GPUs.

Innovation

• GPU-accelerated stochastic optimization library
• Supports multiple predictive control algorithms
• Customizable dynamics models via API