[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-86587-en":3,"doc-seo-86587-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},86587,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","Active Noise Floor Estimation for Reliability-Optimal POMDPs: A Value-of-Noise-Information Approach","Finite Reliability Representations certify when a cell-constant policy suffices for reliable decisions in partially observed systems under a known physical noise floor. In real deployments, the effective noise level is latent and context-dependent, varying across sensing (illumination, reflectivity, occlusion) and execution (terrain, actuator degradation, wheel–ground interaction). This work proposes a certificate-aware active disambiguation framework that quantifies the value of reducing posterior uncertainty in the noise parameter θ to preserve the FRR policy guarantee. VoNI is bounded using certificate mismatch terms and guides diagnostic probing based on innovation and residual information.","Active Noise Floor Estimation for Reliability-Optimal POMDPs: A Value-of-Noise-Information Approach  \nHyung-Jin Yoon  \narXiv :2607 . 11822v1 [ ee ss . SY] 13 Jul 2026  \nAbstract—Finite Reliability Representations (FRR) certify when a cell-constant policy is sufficient for reliable decisionmaking in a partially observed system with a known physical noise floor. In practice, however, the effective noise floor is often latent and context-dependent: sensing uncertainty may vary with illumination, reflectivity, or occlusion, while execution uncertainty may vary with terrain, actuator degradation, or wheel–ground interaction. This paper develops a certificateaware active disambiguation framework for an unknown physical noise parameter θ = (σy ,σu), with the sensor-only case recovered by fixing σu. Rather than treating noise estimation as an end in itself, the proposed framework decides when reducing posterior uncertainty in θ is valuable for preserving the FRR policy certificate. We define the Value of Noise Information (VoNI) as the expected excess FRR certificate gap incurred when the reliability cover is calibrated to the current estimate rather than to the realized physical noise parameter. We bound VoNI using two certificate-relevant mismatch terms: action-value model mismatch and FRR radius inflation. The resulting bound shows that actively refining the noise estimate has low decision value in sub-crossover regimes where the FRR certificate is insensitive to θ, but becomes valuable when posterior uncertainty can invalidate the current reliability cover. A bi-level decision maker takes a posterior over θ obtained from innovation statistics, execution residuals, or a separate online noise estimator, and triggers diagnostic probing only when uncertainty in θ threatens the FRR certificate. We also interpret VoNI as a tractable, certificate-aware approximation to a high-level finite POMDP for disambiguating latent sensing– execution regimes. Under well-specified, stationary, identifiable, and persistently exciting noise regimes, we establish posterior consistency of the noise estimate and convergence of the induced policy loss to the FRR approximation floor. Numerical results on a closed-loop unicycle-model UGV with EKF-based innovation residuals instantiate the sensor-only case and show that VoNI detects abrupt sensing-noise jumps earlier than posteriorentropy probing, tracks gradual sensing-noise drift with lower mean absolute error, and uses substantially fewer diagnostic probing actions across 50 Monte Carlo trials.  \nI. INTRODUCTION  \nPartially observed autonomous systems must plan under two distinct layers of uncertainty: uncertainty about the current state, captured by the belief b ∈ B(X), and uncertainty about the physical channels through which state information is acquired and commands are executed. The theory of Finite Reliability Representations (FRR) [1] addresses the first layer for a known physical noise floor: the deployed sensing and execution model induces a finite cover of the reachable belief space by reliability cells, and a cell-constant policy achieves suboptimality bounded by 2ε/(1 − γ) when every cell has  \nH.-J. Yoon is with the Department of Mechanical and Nuclear Engineering, Tennessee Technological University, Cookeville, TN 37135, USA. [hjyoon@tntech.edu](hjyoon@tntech.edu)  \noptimal action-value variation at most ε . This result connects hardware sensing and actuation capability to the minimum policy resolution needed for reliability-optimal control.  \nFRR, however, treats the physical noise-floor parameter as known. This assumption is violated in practice. The effective noise floor is a function of context: illumination, surface reflectivity, occlusion, relative motion, terrain interaction, wheel slip, and actuator degradation can all change the sensing and execution model in ways that cannot be fully characterized at design time. A stereo camera may provide accurate depth estimates on textured open ","cbCaiu2bRgWrMRf3","https://ap.wps.com/l/cbCaiu2bRgWrMRf3","pdf",439419,4,1,13,"English","en",105,"# Abstract\n# Introduction\n## Uncertainty in belief and physical noise channels\n## Failure of FRR under unknown context-dependent noise\n## Latent noise parameter θ and estimation decision question\n## Complementary relation to disturbance-covariance estimation","[{\"question\":\"What problem does the paper address in reliability-optimal POMDPs?\",\"answer\":\"It addresses how reliability guarantees based on Finite Reliability Representations degrade when the physical noise floor is unknown and context-dependent across sensing and execution channels.\"},{\"question\":\"What is the key idea behind Value of Noise Information (VoNI)?\",\"answer\":\"VoNI measures the expected excess FRR certificate gap incurred when the reliability cover is calibrated to the current estimate rather than to the realized physical noise parameter, guiding when probing information is worthwhile.\"},{\"question\":\"How does the framework decide when to perform diagnostic probing?\",\"answer\":\"A bi-level decision maker forms a posterior over the latent noise parameter θ using innovation statistics and execution residuals (or an online estimator), and triggers probing only when uncertainty in θ threatens the FRR 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problem does the paper address in reliability-optimal POMDPs?","Question",{"text":75,"@type":76},"It addresses how reliability guarantees based on Finite Reliability Representations degrade when the physical noise floor is unknown and context-dependent across sensing and execution channels.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is the key idea behind Value of Noise Information (VoNI)?",{"text":80,"@type":76},"VoNI measures the expected excess FRR certificate gap incurred when the reliability cover is calibrated to the current estimate rather than to the realized physical noise parameter, guiding when probing information is worthwhile.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the framework decide when to perform diagnostic probing?",{"text":84,"@type":76},"A bi-level decision maker forms a posterior over the latent noise parameter θ using innovation statistics and execution residuals (or an online estimator), and triggers probing only when uncertainty 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