analiza aiqbench
This research advances safety-critical control systems by integrating adaptive conformal prediction to generate calibrated, asymmetric uncertainty intervals without assuming specific distribution shapes. By embedding these state-dependent uncertainty sets into probabilistic control barrier functions within a Model Predictive Control framework, the method achieves provable safety guarantees while reducing the conservativeness typical of traditional approaches. The work is significant for robotics and autonomous systems where certifiable performance under unknown dynamics is essential.
Safety-Critical Control under Uncertainty via Adaptive Conformal Quantile Prediction Intervals
arXiv:2609.23280v1 Announce Type: new Abstract: Safety-critical control under uncertainty requires uncertainty representations that are both statistically valid (for certifiable performance) and compatible with enforceable safety constraints. However, existing methods often assume particular distributions of uncertainty for provable safety guarantees or establish symmetric and input-agnostic prediction intervals for robust safety, which can lead to misaligned or overly conservative safety constraints in control synthesis. In this paper, we introduce a novel safe control framework with adaptive uncertainty quantification that constructs calibrated and state-dependent prediction intervals to enable high-probability safety guarantees, while improving constrained control performance. The framework leverages adaptive conformal prediction (ACP) and extends it with conformal quantile regression (CQR) to capture distribution-free, asymmetric uncertainty intervals with certifiable probabilistic coverage, and integrates the resulting uncertainty sets into a probabilistic control barrier function formulation to enforce robust safety with reduced conservativeness. This yields uncertainty-aware safe control constraints that can be incorporated within a model predictive control(MPC) framework to provide provably safe behaviors with high probability. Simulation and theoretical results are provided to demonstrate the effectiveness of our approach.
Abstract:Safety-critical control under uncertainty requires uncertainty representations that are both statistically valid (for certifiable performance) and compatible with enforceable safety constraints. However, existing methods often assume particular distributions of uncertainty for provable safety guarantees or establish symmetric and input-agnostic prediction intervals for robust safety, which can lead to misaligned or overly conservative safety constraints in control synthesis. In this paper, we introduce a novel safe control framework with adaptive uncertainty quantification that constructs calibrated and state-dependent prediction intervals to enable high-probability safety guarantees, while improving constrained control performance. The framework leverages adaptive conformal prediction (ACP) and extends it with conformal quantile regression (CQR) to capture distribution-free, asymmetric uncertainty intervals with certifiable probabilistic coverage, and integrates the resulting uncertainty sets into a probabilistic control barrier function formulation to enforce robust safety with reduced conservativeness. This yields uncertainty-aware safe control constraints that can be incorporated within a model predictive control(MPC) framework to provide provably safe behaviors with high probability. Simulation and theoretical results are provided to demonstrate the effectiveness of our approach.
Robotics (cs.RO)
arXiv:2609.23280 [cs.RO]
(or arXiv:2609.23280v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.23280 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Text integral sub licența deschisă a publicației · 263 cuvinte
Subiecte conexe
Staggered Integral Online Conformal Prediction for Safe Dynamics Adaptation with Multi-Step Coverage Guarantees
arXiv · 12 oct. 2026
SACK: Safe Active Continual Koopman Learning for Uncertain Systems with Contractive Guarantees
arXiv · 6 aug. 2026
Neural Barriers: An Online Certifiable Learning-enhanced Adaptive High Order Safety Critical Control
arXiv · 6 oct. 2026
Adaptive Risk-Certified Event-Triggered Replanning for Dynamic Navigation
arXiv · 8 oct. 2026