Using context to adapt to sensor drift (2024)
Jamieson Warner, Ashwin Devaraj, and Risto Miikkulainen
Lifelong development allows animals and machines to adapt to changes in the environment as well as in their own systems, such as wear and tear in sensors and actuators. An important use case of such adaptation is industrial odor-sensing. Metal-oxide-based sensors can be used to detect gaseous com- pounds in the air; however, the gases interact with the sensors, causing their responses to change over time in a process called sensor drift. Sensor drift is irreversible and requires frequent recalibration with additional data. This paper demonstrates that an adaptive system that represents the drift as context for the skill of odor sensing achieves the same goal automatically. After it is trained on the history of changes, a neural network predicts future contexts, allowing the context+skill sensing system to adapt to sensor drift. Evaluated on an industrial dataset of gas-sensor drift, the approach performed better than standard drift-naive and ensembling methods. In this way, the context+skill system emulates the natural ability of animal olfaction systems to adapt to a changing world, and demonstrates how it can be effective in real-world applications.
In Proceedings of the International Conference on Development and Learning (ICDL 2024), 2024. (also arXiv:2003.07292).

Risto Miikkulainen Faculty risto [at] cs utexas edu
Jamieson Warner Ph.D. Student jamiesonwarner [at] utexas edu
ContextSkillDrift Download on GitHub.

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