Local Conformal Calibration of Dynamics Uncertainty from Semantic Images

• URL: http://arxiv.org/abs/2605.13028v1
• Date: Wed, 13 May 2026 05:33:42 GMT
• Title: Local Conformal Calibration of Dynamics Uncertainty from Semantic Images
• Abstract summary: We introduce Observation-aware Conformal Uncertainty Local-Calibration (OCULAR)<n>OCULAR is a conformal prediction-based algorithm that uses perception information to provide uncertainty quantification guarantees.<n>We validate our algorithm on a double-integrator system subject to random perturbations and significant model mismatch.
• Score: 11.10792616622245
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce Observation-aware Conformal Uncertainty Local-Calibration (OCULAR), a conformal prediction-based algorithm that uses perception information to provide uncertainty quantification guarantees for unseen test-time environments. While previous conformal approaches lack the ability to discriminate between state-action space regions leading to higher or lower model mismatch, and require environment-specific data, our method uses data collected from visually similar environments to provably calibrate a given linear Gaussian dynamics model of arbitrary fidelity. The prediction regions generated from OCULAR are guaranteed to contain the future system states with, at least, a user-set likelihood, despite both aleatoric and epistemic uncertainty -- i.e., uncertainty arising from both stochastic disturbances and lack of data. Our guarantees are non-asymptotic and distribution-free, not requiring strong assumptions about the unknown real system dynamics. Our calibration procedure enables distinguishing between observation-velocity-action inputs leading to higher and lower next-state-uncertainty, which is helpful for probabilistically-safe planning. We numerically validate our algorithm on a double-integrator system subject to random perturbations and significant model mismatch, using both a simplified sensor and a more realistic simulated camera. Our approach appropriately quantifies uncertainty both when in-distribution and out-of-distribution, being comparatively volume-efficient to baselines requiring environment-specific data.

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