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/ .

翻译：本文介绍了一个名为MPPI-Generic的新型C++/CUDA库，用于GPU加速的随机优化。该库提供了模型预测路径积分控制、管式模型预测路径积分控制以及鲁棒模型预测路径积分控制的实现，并允许这些算法应用于多种已有的动力学模型与代价函数。此外，研究人员可根据我们的API定义创建自己的动力学模型或代价函数，而无需修改模型预测路径积分控制的核心代码。最后，我们在多种GPU上对比了本库与其他主流模型预测路径积分控制实现的计算性能，以展示本库所能实现的实时处理能力。库代码可见于：https://acdslab.github.io/mppi-generic-website/。