Risk-Aware Online Conformal State Probing
AI-based autonomous agents, typically hosted at data centers, must acquire state information from robots or edge devices in order to issue informed control decisions. Managing uncertainty about the state is particularly consequential in safety-critical settings, in which average-case guarantees are insufficient. In this context, we study a sequential decision maker process that jointly decides which actions to take and when to probe given access to an arbitrary state prediction model. We propose online conformal state probing (OCSP), an action and probing policy that certifies worst-case reliability levels without relying on distributional assumptions. OCSP is designed to provably control...
| Authors | Pietro Talli, Petar Popovski, Osvaldo Simeone |
|---|---|
| Primary category | eess.SP |
| Published | 22 September 2026 |
| Last updated at source | 22 September 2026 |
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