arXiv:2605.16588 · v2

Policy Library CBF: Finite-Horizon Safety at Runtime via Parallel Rollouts

首次提交 2026-05-16 03:45

版本更新 2026-11-11 05:56

雷达观测 2026-11-13 11:12

摘要

Safety-critical autonomy in unstructured environments poses significant challenges for online safety certification under evolving constraints, particularly when a single backup policy is not effective across operating conditions. We propose the Policy Library Control Barrier Function (PL-CBF), a runtime safety filter that evaluates a library of fallback policies via parallel finite-horizon rollouts. Among policies with constraint-satisfying rollouts and feasible CBF constraints, PL-CBF selects the policy maximizing the volume of its admissible control set and solves a single quadratic program to minimally modify the nominal input within that constrained input set. Using a finite-horizon language metric over closed-loop behaviors, we characterize when a finite policy library contains a constraint-satisfying rollout. Five simulation studies cover indoor robot navigation with a double-integrator model (4 states), highway driving under abrupt friction changes with a nonlinear vehicle model (8 states), and quadrotor flight control among dynamic obstacles (12 states), demonstrating improved safety performance compared with the evaluated baselines while maintaining millisecond-scale mean computation times. Hardware experiments on a mobile robot among moving pedestrians and a custom-built quadrotor among dynamic obstacles demonstrate real-time operation of PL-CBF.

作者备注：Project page: https://www.taekyung.me/plcbf