Guaranteeing constraint satisfaction is challenging in imitation learning (IL), particularly in tasks that require operating near a system's handling limits. Traditional IL methods often struggle to enforce constraints, leading to suboptimal performance in high-precision tasks. In this paper, we present a simple approach to incorporating safety into the IL objective. Through simulations, we empirically validate our approach on an autonomous racing task with both full-state and image feedback, demonstrating improved constraint satisfaction and greater consistency in task performance compared to a baseline method.
View on arXiv@article{cao2025_2503.07737,
title={ A Simple Approach to Constraint-Aware Imitation Learning with Application to Autonomous Racing },
author={ Shengfan Cao and Eunhyek Joa and Francesco Borrelli },
journal={arXiv preprint arXiv:2503.07737},
year={ 2025 }
}