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arXiv · 2309.09870

Zero-Shot Policy Transferability for the Control of a Scale Autonomous Vehicle

Harry Zhang·Stefan Caldararu·Sriram Ashokkumar·Ishaan Mahajan·Aaron Young·Alexis Ruiz·Huzaifa Unjhawala·Luning Bakke·Dan Negrut
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Abstract

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.

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Harry Zhang, Stefan Caldararu, Sriram Ashokkumar, Ishaan Mahajan, Aaron Young, Alexis Ruiz, Huzaifa Unjhawala, Luning Bakke, Dan Negrut. 2023-09-18. Zero-Shot Policy Transferability for the Control of a Scale Autonomous Vehicle. https://arxiv.org/abs/2309.09870

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