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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 forscientific exploration and discovery, including the first discovery of abest-in-class material. To build and use these systems, the next generationworkforce 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 platformto teach the required skills. Industry can also use such a platform fordeveloping and evaluating autonomous physical science methodologies. We presentthe next generation in science education, a kit for building a low-costautonomous scientist. The kit was used during two courses at the University ofMaryland to teach undergraduate and graduate students autonomous physicalscience. We discuss its use in the course and its greater capability to teachthe dual tasks of autonomous model exploration, optimization, anddetermination, with an example of autonomous experimental "discovery" of theHenderson-Hasselbalch equation.

A Low-Cost Robot Science Kit for Education with Symbolic Regression for Hypothesis Discovery and Validation