Conformal recovery-deadline certificates supply distribution-free finite-sample upper bounds on recovery time for adapting controllers in runtime assurance, with proven marginal, weighted, and Mondrian coverage guarantees demonstrated on spacecraft and pendulum testbeds.
Statistically assuring safety of control systems using ensembles of safety filters and conformal prediction
3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3verdicts
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A hybrid spacecraft autonomy architecture combines a grammar-constrained foundation-model commander, rapid motor adaptation controller, and LTL runtime shield, achieving 84% valid actions, 94-98% fault recovery, and 94.5% autonomous operation on a 6-DOF Basilisk testbed.
ACoFi adaptively tunes the switching threshold of learned safety filters using conformal inference on the range of predicted safety values, asymptotically bounding the rate of incorrect safety assessments by a user parameter and reducing violations versus fixed thresholds in simulations.
citing papers explorer
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Conformal Recovery-Deadline Certificates for Runtime Assurance of Adapting Controllers
Conformal recovery-deadline certificates supply distribution-free finite-sample upper bounds on recovery time for adapting controllers in runtime assurance, with proven marginal, weighted, and Mondrian coverage guarantees demonstrated on spacecraft and pendulum testbeds.
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Reliability-Asymmetric Spacecraft Autonomy: Co-Designing a Capable Learned GNC Stack with a Verified, Adaptation-Aware Runtime Shield
A hybrid spacecraft autonomy architecture combines a grammar-constrained foundation-model commander, rapid motor adaptation controller, and LTL runtime shield, achieving 84% valid actions, 94-98% fault recovery, and 94.5% autonomous operation on a 6-DOF Basilisk testbed.
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Safe Control using Learned Safety Filters and Adaptive Conformal Inference
ACoFi adaptively tunes the switching threshold of learned safety filters using conformal inference on the range of predicted safety values, asymptotically bounding the rate of incorrect safety assessments by a user parameter and reducing violations versus fixed thresholds in simulations.