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.
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