Safety filters leveraging control barrier functions (CBFs) are highly effective for enforcing safe behavior on complex systems. It is often easier to synthesize CBFs for a Reduced order Model (RoM), and track the resulting safe behavior on the Full order Model (FoM) -- yet gaps between the RoM and FoM can result in safety violations. This paper introduces \emph{predictive CBFs} to address this gap by leveraging rollouts of the FoM to define a predictive robustness term added to the RoM CBF condition. Theoretically, we prove that this guarantees safety in a layered control implementation. Practically, we learn the predictive robustness term through massive parallel simulation with domain randomization. We demonstrate in simulation that this yields safe FoM behavior with minimal conservatism, and experimentally realize predictive CBFs on a 3D hopping robot.

翻译：利用控制屏障函数（CBFs）的安全滤波器在确保复杂系统安全行为方面极为有效。通常，为降阶模型（RoM）综合CBF并跟踪其在全阶模型（FoM）上产生的安全行为更为简便——然而RoM与FoM之间的差异可能导致安全违规。本文引入**预测CBFs**，通过利用FoM的滚动推演来定义一个预测鲁棒性项，并将其添加到RoM CBF条件中，以弥合这一差距。理论上，我们证明了该方法在分层控制实现中能确保安全性。实践上，我们通过大规模并行仿真与领域随机化来学习该预测鲁棒性项。仿真结果表明，该方法能以最小保守度实现安全的FoM行为，并在三维弹跳机器人上实验验证了预测CBFs的有效性。