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The aim is to evaluate the generalizability of a class-discriminant codebook across robotic and industrial sensors.

June 27, 2026Open Access

A Class-Discriminant Codebook Encoder as a Cross-Domain Sensor Substrate: Robotic Force/Torque, Industrial Vibration, and Articulated-Motion Gesture

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The aim is to evaluate the generalizability of a class-discriminant codebook across robotic and industrial sensors.
Evaluated the encoder on robotic force/torque, industrial vibration, and articulated-motion gesture data.
Used benchmark datasets to characterize encoder performance across various sensor tasks.
Measured classification accuracy and predictive maintenance capabilities on real public datasets.
Achieved ≈ 0.07 AUC at 240× compression for robotic task classification.
Matched a full classifier at 1,834× compression on industrial bearing fault detection with AUC 0.996.
Demonstrated predictive maintenance capabilities with an anomaly score correlating with remaining useful life (RUL).

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Randomized trial demonstrates multi-task inference and anomaly detection in sensors, indicating broad applicability.

Ferlic et al. (2026) studied this question.

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