Active Noise Floor Estimation for Reliability-Optimal POMDPs: A Value-of-Noise-Information Approach
2026-07-13 • Robotics
Robotics
AI summaryⓘ
The authors study how to decide when a simple decision policy is reliable in systems with unknown sensing and execution noise. They introduce a way to measure the value of learning about this noise, called the Value of Noise Information (VoNI), which helps decide when it's worth to actively gather more data. Their method uses uncertainty estimates to trigger extra probing only if the unknown noise might make the current reliability guarantee invalid. In simulations with unmanned vehicles, their approach detects noise changes sooner and uses fewer probes compared to traditional methods.
Finite Reliability RepresentationsPartially Observed SystemsSensor NoiseExecution NoiseValue of Noise InformationPosterior UncertaintyActive DisambiguationExtended Kalman FilterMonte Carlo SimulationDecision-Making Under Uncertainty
Authors
Hyung-Jin Yoon
Abstract
Finite Reliability Representations (FRR) certify when a cell-constant policy is sufficient for reliable decision-making in a partially observed system with a known physical noise floor. In practice, however, sensing and execution noise can be latent and context-dependent. This paper develops a certificate-aware active disambiguation framework for an unknown physical noise parameter theta = (sigma_y, sigma_u), with the sensor-only case obtained by fixing sigma_u. We define the Value of Noise Information (VoNI) as the expected excess FRR certificate gap caused by using a reliability cover calibrated to the current estimate rather than to the realized noise parameter. We bound VoNI using action-value model mismatch and FRR radius inflation, showing that noise estimation has low decision value in sub-crossover regimes where the FRR certificate is insensitive to theta, but becomes valuable when posterior uncertainty can invalidate the current cover. A bi-level decision maker uses a posterior over theta, obtained from innovation statistics, execution residuals, or another online estimator, and triggers diagnostic probing only when uncertainty threatens the FRR certificate. We also interpret VoNI as a tractable, certificate-aware approximation to a high-level finite POMDP for latent sensing-execution regime disambiguation. Under stationary, identifiable, and persistently exciting regimes, we establish posterior consistency and convergence of the induced policy loss to the FRR approximation floor. Closed-loop UGV simulations with EKF-based innovation residuals show earlier detection of abrupt sensing-noise jumps, lower drift-tracking error, and substantially fewer probing actions than posterior-entropy exploration over 50 Monte Carlo trials.