Uncertainty-Aware Predictive Safety Filters for Probabilistic Neural Network Dynamics
Predictive safety filters (PSFs) leverage model predictive control to enforce constraint satisfaction during deep reinforcement learning (RL) exploration, yet their reliance on first-principles models or Gaussian processes limits scalability and broader applicability. Meanwhile, model-based RL (MBRL) methods routinely employ probabilistic ensemble (PE) neural networks to capture complex, high-dimensional dynamics from data with minimal prior knowledge. However, existing attempts to integrate PEs into PSFs lack rigorous uncertainty quantification. We introduce the Uncertainty-Aware Predictive Safety Filter (UPSi), a PSF that provides rigorous safety verification using PE dynamics models by...
| Authors | Bernd Frauenknecht, Lukas Kesper, Daniel Mayfrank et al. |
|---|---|
| Primary category | cs.LG |
| Published | 29 April 2026 |
| Last updated at source | 10 August 2026 |
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