March 13, 2024, 4:42 a.m. | Shaoru Chen, Lekan Molu, Mahyar Fazlyab

cs.LG updates on arXiv.org arxiv.org

arXiv:2403.07308v1 Announce Type: new
Abstract: Barrier functions are a general framework for establishing a safety guarantee for a system. However, there is no general method for finding these functions. To address this shortcoming, recent approaches use self-supervised learning techniques to learn these functions using training data that are periodically generated by a verification procedure, leading to a verification-aided learning framework. Despite its immense potential in automating barrier function synthesis, the verification-aided learning framework does not have termination guarantees and may …

abstract arxiv cs.ai cs.lg cs.sy data eess.sy framework functions general generated however learn network neural network safety self-supervised learning supervised learning training training data type verification

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