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Sim2Real Transfer of Imitation Learning of Motion Control for Car-like Mobile Robots Using Digital Twin Testbed
Journal article   Open access   Peer reviewed

Sim2Real Transfer of Imitation Learning of Motion Control for Car-like Mobile Robots Using Digital Twin Testbed

Narges Mohaghegh, Hai Wang and Amirmehdi Yazdani
Robotics (Basel), Vol.14(12), 180
2025
DOI: https://doi.org/10.3390/robotics14120180
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CC BY V4.0,  Open Access

Abstract

digital twin imitation learning motion tracking MPC-Backstepping controller neural network controller
Reliable transfer of control policies from simulation to real-world robotic systems remains a central challenge in robotics, particularly for car-like mobile robots. Digital Twin (DT) technology provides a robust framework for high-fidelity replication of physical platforms and bi-directional synchronization between virtual and real environments. In this study, a DT-based testbed is developed to train and evaluate an imitation learning (IL) control framework in which a neural network policy learns to replicate the behavior of a hybrid Model Predictive Control (MPC)–Backstepping expert controller. The DT framework ensures consistent benchmarking between simulated and physical execution, supporting a structured and safe process for policy validation and deployment. Experimental analysis demonstrates that the learned policy effectively reproduces expert behavior, achieving bounded trajectory-tracking errors and stable performance across simulation and real-world tests. The results confirm that DT-enabled IL provides a viable pathway for Sim2Real transfer, accelerating controller development and deployment in autonomous mobile robotics.

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UN Sustainable Development Goals (SDGs)

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#9 Industry, Innovation and Infrastructure
#12 Responsible Consumption & Production

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