pith:UOGG4A7B
Uncertainty-Aware Predictive Safety Filters for Probabilistic Neural Network Dynamics
The Uncertainty-Aware Predictive Safety Filter uses reachable sets from probabilistic ensemble neural networks and an explicit certainty constraint to guarantee safety during model-based reinforcement learning exploration.
arxiv:2604.26836 v2 · 2026-04-29 · cs.LG · cs.SY · eess.SY
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Claims
We introduce the Uncertainty-Aware Predictive Safety Filter (UPSi), a PSF that provides rigorous safety predictions using PE dynamics models by formulating future outcomes as reachable sets. UPSi introduces an explicit certainty constraint that prevents model exploitation and integrates seamlessly into common MBRL frameworks.
That reachable sets derived from probabilistic ensemble neural network predictions can be computed rigorously enough to guarantee constraint satisfaction, and that the certainty constraint sufficiently prevents exploitation of model uncertainty without overly restricting exploration.
UPSi integrates probabilistic neural network dynamics into predictive safety filters via reachable sets and a certainty constraint, improving safety in model-based RL exploration while matching standard performance.
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| First computed | 2026-05-22T02:04:41.422199Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
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Canonical record JSON
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