Testing POMDP Policies Against Sensor Drift and Model Mismatch
New framework quantifies how much observation noise a decision policy can tolerate before performance collapses, with polynomial-time algorithms for real systems.
Source: arxiv/cs.AI · Benjamin Kraske, Qi Heng Ho, Federico Rossi, Morteza Lahijanian, Zachary Sunberg · open original ↗
Kraske et al. propose methods to measure and guarantee POMDP policy robustness when sensor models drift from their design assumptions.
- — POMDP policies trained on nominal sensor models often fail when real sensors degrade or drift during deployment.
- — The Policy Observation Robustness Problem finds the maximum allowable sensor deviation before policy value drops below a threshold.
- — Two variants exist: sticky (state/action-dependent noise) and non-sticky (history-dependent noise) observation perturbations.
- — Bi-level optimization with monotonic inner structure enables root-finding solutions with polynomial complexity in non-sticky case.
- — Finite-state controller policies reduce search space by depending only on controller nodes, not full observation histories.
- — Robust Interval Search algorithm provides soundness and convergence guarantees for both variants.
- — Experiments scale to tens of thousands of states; robotics and operations research case studies show practical applicability.
Astrobobo tool mapping
- Knowledge Capture Document your system's nominal sensor model and known failure modes (drift, noise, latency). Record the performance threshold below which the policy is unacceptable.
- Focus Brief Summarize the bi-level optimization structure and root-finding approach so your team can evaluate whether to implement Robust Interval Search or use an existing robustness library.
- Reading Queue Queue related work on adversarial robustness in RL and sensor fusion to understand how this POMDP result fits into broader uncertainty quantification.
Frequently asked
- A POMDP (Partially Observable Markov Decision Process) is a decision-making model where the agent cannot see the true system state directly—only noisy observations. A policy trained on clean observations may fail when real sensors degrade, producing noisier or drifted readings. This work quantifies how much noise a policy can tolerate before its performance collapses.
cite ▸
APA
Benjamin Kraske, Qi Heng Ho, Federico Rossi, Morteza Lahijanian, Zachary Sunberg. (2026, April 26). Testing POMDP Policies Against Sensor Drift and Model Mismatch. Astrobobo Content Engine (rewrite of arxiv/cs.AI). https://astrobobo-content-engine.vercel.app/article/testing-pomdp-policies-against-sensor-drift-and-model-mismatch-0c9bce
MLA
Benjamin Kraske, Qi Heng Ho, Federico Rossi, Morteza Lahijanian, Zachary Sunberg. "Testing POMDP Policies Against Sensor Drift and Model Mismatch." Astrobobo Content Engine, 26 Apr 2026, https://astrobobo-content-engine.vercel.app/article/testing-pomdp-policies-against-sensor-drift-and-model-mismatch-0c9bce. Based on "arxiv/cs.AI", https://arxiv.org/abs/2604.21256.
BibTeX
@misc{astrobobo_testing-pomdp-policies-against-sensor-drift-and-model-mismatch-0c9bce_2026,
author = {Benjamin Kraske, Qi Heng Ho, Federico Rossi, Morteza Lahijanian, Zachary Sunberg},
title = {Testing POMDP Policies Against Sensor Drift and Model Mismatch},
year = {2026},
url = {https://astrobobo-content-engine.vercel.app/article/testing-pomdp-policies-against-sensor-drift-and-model-mismatch-0c9bce},
note = {Astrobobo rewrite of arxiv/cs.AI, https://arxiv.org/abs/2604.21256},
}