用视觉模型隐空间构建安全控制器，减少人工标注依赖。
Safety Certification in the Latent space using Control Barrier Functions and World Models
- 在世界模型隐空间中学习控制屏障函数与安全控制器
- 仅需少量标注数据，结合视觉变换器预测动态
- 适合需要安全控制的机器人视觉系统开发
从视觉数据合成安全控制器通常需要大量对安全关键数据的手动标注，这在现实场景中往往不切实际。世界模型的最新进展使得在隐空间中实现可靠的预测成为可能，为可扩展且数据高效的安全部署提供了新路径。本文提出一种半监督框架，利用世界模型隐空间中学习到的控制屏障证书（CBCs）来合成安全的视觉运动策略。该方法联合学习神经屏障函数与安全控制器，仅依赖有限的标注数据，同时利用现代视觉变换器的强大预测能力进行隐空间动态建模。
原文摘要 · Abstract (English)
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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