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.
Robotics: Science and Systems (RSS) , year =
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
CPSS projects cumulative safety constraints into time-varying per-state thresholds for online action shielding in nonstationary RL, providing per-state guarantees and cumulative bounds.
LILAC+ combines context-based, adaptation-speed, and budget-to-state safety constraints to reduce violations in continual RL under nonstationary conditions, demonstrated in simulated driving tasks.
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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From Cumulative Constraints to Adaptive Runtime Safety Control for Nonstationary Reinforcement Learning
CPSS projects cumulative safety constraints into time-varying per-state thresholds for online action shielding in nonstationary RL, providing per-state guarantees and cumulative bounds.
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Safe Continual Reinforcement Learning under Nonstationarity via Adaptive Safety Constraints
LILAC+ combines context-based, adaptation-speed, and budget-to-state safety constraints to reduce violations in continual RL under nonstationary conditions, demonstrated in simulated driving tasks.