Control of machine learning models has emerged as an important paradigm for a broad range of robotics applications. In this paper, we present a sampling-based nonlinear model predictive control (NMPC) approach for control of neural network dynamics. We show its design in two parts: 1) formulating conventional optimization-based NMPC as a Bayesian state estimation problem, and 2) using particle filtering/smoothing to achieve the estimation. Through a principled sampling-based implementation, this approach can potentially make effective searches in the control action space for optimal control and also facilitate computation toward overcoming the challenges caused by neural network dynamics. We apply the proposed NMPC approach to motion planning for autonomous vehicles. The specific problem considers nonlinear unknown vehicle dynamics modeled as neural networks as well as dynamic on-road driving scenarios. The approach shows significant effectiveness in successful motion planning in case studies.
翻译:对机器学习模型的控制已成为一系列机器人应用的重要范例。本文介绍了一种基于取样的非线性模型预测控制(NMPC)控制神经网络动态的方法。我们将其分为两部分展示:一是将常规优化型NMPC作为巴伊西亚州估算问题,二是利用粒子过滤/移动来实现估算。通过有原则的基于抽样的实施,这一方法可以在控制行动空间中进行有效搜索,以便实现最佳控制,并便利计算以克服神经网络动态带来的挑战。我们将拟议的NMPC方法应用于自主车辆的机动规划。具体问题认为非线性未知的车辆动态作为神经网络模型以及动态的公路驾驶假想。该方法在案例研究中的成功动作规划中显示了显著的成效。