Sim2real for Autonomous Vehicle Control using Executable Digital Twin

TL;DR

This paper introduces a sim2real transfer method for autonomous vehicle control using an executable digital twin, enabling efficient adaptation of controllers from simulation to real-world vehicles without extensive tuning.

Contribution

The paper presents a novel sim2real framework leveraging high fidelity simulation, data-driven estimation, and online adaptation for nonlinear model predictive control in autonomous vehicles.

Findings

Achieved high control performance without extensive tuning.

Successfully adapted controllers from simulation to real vehicles.

Utilized online high fidelity simulation for real-time adaptation.

Abstract

In this work, we propose a sim2real method to transfer and adapt a nonlinear model predictive controller (NMPC) from simulation to the real target system based on executable digital twin (xDT). The xDT model is a high fidelity vehicle dynamics simulator, executable online in the control parameter randomization and learning process. The parameters are adapted to gradually improve control performance and deal with changing real-world environment. In particular, the performance metric is not required to be differentiable nor analytical with respect to the control parameters and system dynamics are not necessary linearized. Eventually, the proposed sim2real framework leverages altogether online high fidelity simulator, data-driven estimations, and simulation based optimization to transfer and adapt efficiently a controller developed in simulation environment to the real platform. Our experiment demonstrates that a high control performance is achieved without tedious time and labor consuming tuning.