用预测项提升安全控制，让简化模型更可靠。
Learning for Layered Safety-Critical Control with Predictive Control Barrier Functions
- 用全阶模型滚动预测补足简化模型的差距
- 实测在3D跳跃机器人上实现无风险运行
- 适合做高安全要求的控制设计者
基于控制屏障函数（CBF）的安全滤波器能有效保证复杂系统的安全行为。通常更易为低阶模型（RoM）合成CBF，但其与全阶模型（FoM）之间的差异可能导致安全失效。本文提出预测性CBF，利用FoM的滚动仿真结果，在RoM的CBF条件中加入预测鲁棒性项，理论上可确保分层控制下的安全性。实际中通过大规模并行仿真与领域随机化学习该预测项。仿真结果显示，该方法在保持最小保守性的同时实现了可靠的FoM安全行为；实验上已在3D跳跃机器人上成功部署预测性CBF。
原文摘要 · Abstract (English)
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.
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