Abstract:Rigorous uncertainty quantification is essential for the safe deployment of autonomous systems in unconstrained environments. Conformal Prediction (CP) provides a distribution-free framework for this task, yet its standard formulations rely on exchangeability assumptions that are violated by the distribution shifts inherent in real-world robotics. Existing online CP methods maintain target coverage by adaptively scaling the conformal threshold, but typically employ a static nonconformity score function. We show that this fixed geometry leads to highly conservative, volume-inefficient prediction regions when environments undergo structural shifts. To address this, we propose $\textbf{AdaptNC}$, a framework for the joint online adaptation of both the nonconformity score parameters and the conformal threshold. AdaptNC leverages an adaptive reweighting scheme to optimize score functions, and introduces a replay buffer mechanism to mitigate the coverage instability that occurs during score transitions. We evaluate AdaptNC on diverse robotic benchmarks involving multi-agent policy changes, environmental changes and sensor degradation. Our results demonstrate that AdaptNC significantly reduces prediction region volume compared to state-of-the-art threshold-only baselines while maintaining target coverage levels.
| Subjects: | Machine Learning (cs.LG); Robotics (cs.RO); Systems and Control (eess.SY) |
| Cite as: | arXiv:2602.01629 [cs.LG] |
| (or arXiv:2602.01629v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2602.01629 arXiv-issued DOI via DataCite |
Submission history
From: Renukanandan Tumu [view email]
[v1]
Mon, 2 Feb 2026 04:41:35 UTC (2,811 KB)
[v2]
Wed, 13 May 2026 17:47:19 UTC (2,784 KB)