A distributionally robust safety filter reduces certification for nonlinear systems under arbitrary uncertainties to a one-dimensional switching-time search with Wasserstein-inflated sampling guarantees.
Backup-Based Safety Filters: A Comparative Review of Backup CBF, Model Predictive Shielding, and gatekeeper
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
abstract
This paper revisits three backup-based safety filters -- Backup Control Barrier Functions (Backup CBF), Model Predictive Shielding (MPS), and gatekeeper -- through a unified comparative framework. Using a common safety-filter abstraction and shared notation, we make explicit both their common backup-policy structure and their key algorithmic differences. We compare the three methods through their filter-inactive sets, i.e., the states where the nominal policy is left unchanged. In particular, we show that MPS is a special case of gatekeeper, and we further relate gatekeeper to the interior of the Backup CBF inactive set within the implicit safe set. This unified view also highlights a key source of conservatism in backup-based safety filters: safety is often evaluated through the feasibility of a backup maneuver, rather than through the nominal policy's continued safe execution. The paper is intended as a compact tutorial and review that clarifies the theoretical connections and differences among these methods.
fields
cs.RO 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
PL-CBF evaluates a library of fallback policies through parallel finite-horizon rollouts and selects the least invasive safe mode via quadratic programming to certify finite-horizon safety at runtime.
Integrates RL with a differentiable CVaR quadratic-program safety layer to jointly learn nominal controls, risk levels, and margins for adaptive safe navigation under motion uncertainty.
citing papers explorer
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