Safety Certification in the Latent space using Control Barrier Functions and World Models

Mehul Anand; Shishir Kolathaya

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arXiv · 2507.13871

Safety Certification in the Latent space using Control Barrier Functions and World Models

Mehul Anand·Shishir Kolathaya

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Abstract

Synthesising safe controllers from visual data typically requires extensive supervised labelling of safety-critical data, which is often impractical in real-world settings. Recent advances in world models enable reliable prediction in latent spaces, opening new avenues for scalable and data-efficient safe control. In this work, we introduce a semi-supervised framework that leverages control barrier certificates (CBCs) learned in the latent space of a world model to synthesise safe visuomotor policies. Our approach jointly learns a neural barrier function and a safe controller using limited labelled data, while exploiting the predictive power of modern vision transformers for latent dynamics modelling.

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Mehul Anand, Shishir Kolathaya. 2025-07-18. Safety Certification in the Latent space using Control Barrier Functions and World Models. https://arxiv.org/abs/2507.13871

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