A Low-Cost Robot Science Kit for Education with Symbolic Regression for Hypothesis Discovery and Validation
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by
Logan Saar, Haotong Liang, Alex Wang, Austin McDannald, Efrain Rodriguez, Ichiro Takeuchi, A. Gilad Kusne
2022
Abstract
The next generation of physical science involves robot scientists -
autonomous physical science systems capable of experimental design, execution,
and analysis in a closed loop. Such systems have shown real-world success for
scientific exploration and discovery, including the first discovery of a
best-in-class material. To build and use these systems, the next generation
workforce requires expertise in diverse areas including ML, control systems,
measurement science, materials synthesis, decision theory, among others.
However, education is lagging. Educators need a low-cost, easy-to-use platform
to teach the required skills. Industry can also use such a platform for
developing and evaluating autonomous physical science methodologies. We present
the next generation in science education, a kit for building a low-cost
autonomous scientist. The kit was used during two courses at the University of
Maryland to teach undergraduate and graduate students autonomous physical
science. We discuss its use in the course and its greater capability to teach
the dual tasks of autonomous model exploration, optimization, and
determination, with an example of autonomous experimental "discovery" of the
Henderson-Hasselbalch equation.
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