[2606.04185] Distribution-Free Risk-Aware Planning and Control Under Uncertainty Using Conformal Spectral Risk Control
[Submitted on 2 Jun 2026]
Title:Distribution-Free Risk-Aware Planning and Control Under Uncertainty Using Conformal Spectral Risk Control
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Abstract:Safe navigation in dynamic and uncertain environments often relies on accurate estimation of, or assumptions about, the true underlying uncertainty. However, accurately characterizing the true uncertainty distribution is often difficult due to limited data or imperfect information. An incorrect understanding of the uncertainty and its associated risk may lead to dangerous decisions even under high levels of risk aversion. To address this issue, we propose a risk-aware model predictive control (RA-MPC) framework that incorporates prediction sets to guarantee risk control below a user-specified threshold without requiring assumptions about the underlying uncertainty distribution. To generate the prediction sets, we develop a distribution-free risk quantification framework that extends conformal risk control (CRC) to general spectral risk measures. We then show that incorporating the prediction sets into the MPC framework provides statistical safety guarantees in terms of spectral risk constraint satisfaction even under uncertainty misspecification. We validate the proposed framework in simulated vehicle obstacle avoidance scenarios, demonstrating improved safety and reduced solve time compared to a baseline RA-MPC framework.
Comments: Submitted to IEEE Robotics and Automation Letters
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2606.04185 [cs.RO]
(or arXiv:2606.04185v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.04185
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Junsik Eom [view email] [v1] Tue, 2 Jun 2026 20:07:56 UTC (168 KB)
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