A Simple Approach to Constraint-Aware Imitation Learning with Application to Autonomous Racing

• URL: http://arxiv.org/abs/2503.07737v1
• Date: Mon, 10 Mar 2025 18:00:16 GMT
• Title: A Simple Approach to Constraint-Aware Imitation Learning with Application to Autonomous Racing
• Authors: Shengfan Cao, Eunhyek Joa, Francesco Borrelli,
• Abstract summary: We present a simple approach to incorporating safety into imitation learning (IL)<n>We empirically validate our approach on an autonomous racing task with both full-state and image feedback.
• Score: 4.755527819500743
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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