We report on a study that employs an in-house developed simulation infrastructure to accomplish zero shot policy transferability for a control policy associated with a scale autonomous vehicle. We focus on implementing policies that require no real world data to be trained (Zero-Shot Transfer), and are developed in-house as opposed to being validated by previous works. We do this by implementing a Neural Network (NN) controller that is trained only on a family of circular reference trajectories. The sensors used are RTK-GPS and IMU, the latter for providing heading. The NN controller is trained using either a human driver (via human in the loop simulation), or a Model Predictive Control (MPC) strategy. We demonstrate these two approaches in conjunction with two operation scenarios: the vehicle follows a waypoint-defined trajectory at constant speed; and the vehicle follows a speed profile that changes along the vehicle's waypoint-defined trajectory. The primary contribution of this work is the demonstration of Zero-Shot Transfer in conjunction with a novel feed-forward NN controller trained using a general purpose, in-house developed simulation platform.
@article{arxiv.2309.09870,
title = {Zero-Shot Policy Transferability for the Control of a Scale Autonomous Vehicle},
author = {Harry Zhang and Stefan Caldararu and Sriram Ashokkumar and Ishaan Mahajan and Aaron Young and Alexis Ruiz and Huzaifa Unjhawala and Luning Bakke and Dan Negrut},
journal= {arXiv preprint arXiv:2309.09870},
year = {2023}
}