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OcclusionCBF for Occluded Dynamic Obstacles

Autonomous exploration is not only about where the robot moves, but also where it looks. With a finite-FoV sensor, a robot may move toward locally unknown space while its sensor is looking elsewhere, causing hidden obstacles to be detected too late.
SEAMLiS is a plug-in safety layer for decentralized multi-robot exploration. It keeps the upstream planner unchanged, but filters the robot’s yaw and acceleration commands so that hidden obstacles are observed early enough for the safety controller to react.
Motivation

SEAMLiS: Safe Multi-Robot Exploration

Online Adaptive CBF

[CDC 2026] Review of Backup-Based Safety Filters

We present Policy Library Control Barrier Function (PL-CBF), a runtime safety filter for autonomous systems operating under evolving constraints and parameter-dependent dynamics. Unlike single-fallback safety filters, PL-CBF retains a finite library of candidate closed-loop policies and certifies safety whenever at least one library policy remains safe over the planning horizon. The method certifies safety on the fly; no offline value function computation. We validate PL-CBF on a planar double-integrator, highway driving with abrupt friction changes (8 states), and 3D quadrotor navigation in crowded dynamic environments (12 states), showing improved safety over single-policy safety filters while retaining millisecond-level runtime.  
Motivation