论文标题
低成本的机器人科学套件,用于教育,并具有符号回归的假设发现和验证
A Low-Cost Robot Science Kit for Education with Symbolic Regression for Hypothesis Discovery and Validation
论文作者
论文摘要
下一代物理科学涉及机器人科学家 - 自主物理科学系统,能够在封闭环中实验设计,执行和分析。这种系统在科学探索和发现方面显示了现实世界中的成功,包括首次发现一类材料。为了构建和使用这些系统,下一代劳动力需要在ML,控制系统,测量科学,材料综合,决策理论等不同领域的专业知识。但是,教育滞后。教育工作者需要一个低成本,易于使用的平台来教授所需的技能。行业还可以使用这样的平台来开发和评估自主物理科学方法论。我们介绍了科学教育的下一代,这是一种建立低成本自治科学家的套件。该套件在马里兰州大学的两门课程中使用,以教本科生和研究生自治物理科学。我们以自主模型探索,优化和确定的双重任务来讨论其在课程中的用途及其更大的能力,并以自主实验的“发现”为例。
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.