Learning Vision-Based Neural Network Controllers with Semi-Probabilistic Safety Guarantees

• URL: http://arxiv.org/abs/2503.00191v1
• Date: Fri, 28 Feb 2025 21:16:42 GMT
• Title: Learning Vision-Based Neural Network Controllers with Semi-Probabilistic Safety Guarantees
• Authors: Xinhang Ma, Junlin Wu, Hussein Sibai, Yiannis Kantaros, Yevgeniy Vorobeychik,
• Abstract summary: We introduce a novel semi-probabilistic verification framework that integrates reachability analysis with conditional generative adversarial networks.<n>Next, we develop a gradient-based training approach that employs a novel safety loss function, safety-aware data-sampling strategy, and curriculum learning.<n> Empirical evaluations in X-Plane 11 airplane landing simulation, CARLA-simulated autonomous lane following, and F1Tenth lane following in a visually-rich miniature environment demonstrate the effectiveness of our method in achieving formal safety guarantees.
• Score: 24.650302053973142
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Ensuring safety in autonomous systems with vision-based control remains a critical challenge due to the high dimensionality of image inputs and the fact that the relationship between true system state and its visual manifestation is unknown. Existing methods for learning-based control in such settings typically lack formal safety guarantees. To address this challenge, we introduce a novel semi-probabilistic verification framework that integrates reachability analysis with conditional generative adversarial networks and distribution-free tail bounds to enable efficient and scalable verification of vision-based neural network controllers. Next, we develop a gradient-based training approach that employs a novel safety loss function, safety-aware data-sampling strategy to efficiently select and store critical training examples, and curriculum learning, to efficiently synthesize safe controllers in the semi-probabilistic framework. Empirical evaluations in X-Plane 11 airplane landing simulation, CARLA-simulated autonomous lane following, and F1Tenth lane following in a physical visually-rich miniature environment demonstrate the effectiveness of our method in achieving formal safety guarantees while maintaining strong nominal performance. Our code is available at https://github.com/xhOwenMa/SPVT.

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