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Neural Safety Filters: Scaling Safety-Critical Control Without Surrendering Guarantees
Jaime Fernández Fisac · Princeton University
The expanding reach of robotics and AI brings acute reliability challenges under limited information, rapidly changing conditions and close human interaction. Safety filters, runtime schemes that monitor a system's candidate actions and intervene to preempt catastrophic failures, have attracted wide interest, but the restrictive assumptions of model-based approaches and the weaker guarantees of data-driven alternatives have reinforced the perception of a fundamental safety-performance tradeoff. Jaime Fernández Fisac presents theoretical and experimental evidence against that view. Building on a unified understanding of safety filter mechanics, he introduces a general class of neural safety filters that extend the rigorous guarantees of safety-critical control beyond the reach of classical synthesis. Safety over a specified operational design domain (ODD) is cast as an adversarial reinforcement learning problem, co-training the system's controller against the worst environment realisations consistent with the ODD. These neural approximations enable certified safety filters in domains from legged locomotion to high-speed racing, including the synthesis of control barrier functions for general nonlinear systems with only black-box access to their dynamics. Wrapped around safety-agnostic planning and learning pipelines, the filters achieve near-optimal task performance in safety-critical ODDs while provably enforcing zero catastrophic failures. The talk then turns to human-centred systems, where safety requires steering interactions away from downstream harms, from anticipating black-swan road encounters to safeguarding novice human operators, and closes with early results on dynamic guardrails for generative AI assistants and open challenges for safety-critical intelligence beyond robotics.
Series: Laboratory for Computational Sensing + Robotics, Johns Hopkins UniversityArtificial IntelligenceRobotics
