
Robotic systems often encounter weak, noisy, partial, and locally ambiguous signals before a clearer anomaly becomes legible. A drive system may show slight current asymmetry, a manipulator may exhibit repeated micro-slip under otherwise plausible contact, or an environmental obstruction may appear inconsistently across perceptual routes. In such cases, the architectural problem is not only detection, but promotion control: how weak findings should influence attention before they are allowed to become stronger operational consequence. This paper argues that robotics anomaly sensing is a suitable application domain for a stratified weak-signal architecture that separates bounded observation, provisional interpretation, short-horizon attentional escalation, and governed promotion. The contribution is narrow and operational. The paper defines a minimal transfer contract for embodied anomaly handling: a light state ladder, a bounded event object, and illustrative promotion logic showing how weak signals may influence inspection before they justify stronger commitment. It does not propose a full robotics stack, semantic SLAM system, safety-certified controller, or universal diagnosis engine. Its narrower claim is that embodied systems need a disciplined place for weak anomaly signals to matter before they harden into motion change, maintenance significance, or ignored noise.
Architectural bridge paper in the broader Spanda / weak-signal interpretation series. This note argues that robotics anomaly sensing is a suitable application domain for a stratified weak-signal architecture in which bounded findings raise attention first, candidate meanings remain provisional, and stronger consequence occurs only through persistence, convergence, and governance. Note: Related source papers and architectural context for this bridge note are available in the Spanda architectural framework repository: https://github.com/putmanmodel/spanda-architectural-framework
robotics, sensor fusion, early warning, weak signals, multi-sensor systems, embodied AI, Robotics, interpretable AI, anomaly detection, governance, autonomous systems, robotics safety, anomaly sensing
robotics, sensor fusion, early warning, weak signals, multi-sensor systems, embodied AI, Robotics, interpretable AI, anomaly detection, governance, autonomous systems, robotics safety, anomaly sensing
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