A conformal prediction certification for belief-space safety filters focuses verification on reliable inference regions to produce less conservative yet high-probability safe filters than standard baselines in human-vehicle simulations.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Approximates encountered state distribution via VAE and constructs dual bound barrier certificates to provide probably approximately safe guarantees in RL by optimizing the non-robust region.
citing papers explorer
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Permissive Safety Through Trusted Inference: Verifiable Belief-Space Neural Safety Filters for Assured Interactive Robotics
A conformal prediction certification for belief-space safety filters focuses verification on reliable inference regions to produce less conservative yet high-probability safe filters than standard baselines in human-vehicle simulations.
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Scenario Generation for Risk-Aware Reinforcement Learning with Probably Approximately Safe Guarantees
Approximates encountered state distribution via VAE and constructs dual bound barrier certificates to provide probably approximately safe guarantees in RL by optimizing the non-robust region.