A LEGO Based Low-Cost Autonomous Scientist: Using Machine Learning to Derive the Henderson-Hasselbalch Equation

The University of Tennessee-Chattanooga chapter of the Society of Physics Students (SPS) has constructed a LEGO-based, low-cost autonomous scientist (LEGOLAS). It is a robot that uses machine learning techniques to autonomously derive the Henderson-Hasselbalch equation, reducing the tedious experimentation and calculations typically involved in acid-base experiments. Originally, it was developed via collaboration between NIST and UMD scientists as a teaching tool to introduce students to machine learning techniques and algorithms [1].

Our chapter decided to build the robot from scratch, instead of purchasing a preassembled model. It became our 2024 Research Project, that was funded by the SPS National research grant and took 2 semesters to complete. It was our second LEGO-themed project. We really enjoyed working on it and along the way learned about 3D printing; electronics such as Raspberry Pi’s, Arduino computers, and pH sensors; Bayesian statistics, Gaussian process regression, and other topics and skills that are usually not taught in undergraduate curriculum. We plan to use this model to introduce students to machine learning, develop other autonomous experiments, and for departmental recruitment and outreach. In this talk, we will share our experiences and lessons we have learned while constructing, calibrating, troubleshooting, and using LEGOLAS.