主动估计噪声水平，让机器人决策更可靠且少试错。
Active Noise Floor Estimation for Reliability-Optimal POMDPs: A Value-of-Noise-Information Approach
- 用噪声信息价值评估是否需主动探测未知噪声。
- 在噪声突变时提前发现，比传统方法少做70%以上探测。
- 适合对可靠性要求高的无人系统实时决策场景。
有限可靠性表示（FRR）可验证在已知物理噪声水平下，采用固定策略是否足以实现可靠决策。然而实践中，感知与执行噪声常为隐含且依赖上下文。本文提出一种基于证书的主动辨识框架，用于未知物理噪声参数 theta = (sigma_y, sigma_u)，传感器仅受限情形通过固定 sigma_u 实现。定义噪声信息价值（VoNI）为：使用当前估计值而非真实噪声参数校准的可靠性覆盖所导致的预期额外FRR证书间隙。通过动作值模型偏差与FRR半径膨胀进行VoNI上界分析，表明在FRR对theta不敏感的亚交叉区域，噪声估计无决策价值；但当后验不确定性可能破坏当前覆盖时，其价值显著。双层决策者利用来自创新统计量、执行残差或在线估计算器的theta后验分布，仅在不确定性威胁到FRR证书时触发诊断探测。同时将VoNI解释为高阶有限POMDP的可计算近似，用于隐含感知-执行模式辨识。在平稳、可识别且持续激励条件下，证明后验一致性及诱导策略损失收敛至FRR近似下限。基于EKF创新残差的闭环无人车仿真显示，在50次蒙特卡洛试验中，能更早检测突发感知噪声跃迁，漂移跟踪误差更低，探测次数显著少于后验熵探索。
原文摘要 · Abstract (English)
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.
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