Sampling-Based Nonlinear MPC of Neural Network Dynamics with Application to Autonomous Vehicle Motion Planning

TL;DR

This paper introduces a sampling-based nonlinear model predictive control method for neural network dynamics, applied to autonomous vehicle motion planning, enabling effective control in complex, unknown nonlinear systems.

Contribution

It formulates NMPC as a Bayesian state estimation problem and employs particle filtering, providing a novel approach to control neural network modeled dynamics.

Findings

Successful motion planning in autonomous vehicle scenarios

Effective control of neural network dynamics demonstrated

Overcoming neural network complexity in control tasks

Abstract

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.