The Use of AI-Robotic Systems for Scientific Discovery
Preprint, 2026
The process of developing theories and models and testing them with experiments is fundamental to the scientific method. Automating the entire scientific method then requires not only automation of the induction of theories from data, but also experimentation from design to implementation. This is the idea behind a robot scientist -- a coupled system of AI and laboratory robotics that has agency to test hypotheses with real-world experiments. In this chapter we explore some of the fundamentals of robot scientists in the philosophy of science. We also map the activities of a robot scientist to machine learning paradigms, and argue that the scientific method shares an analogy with active learning. We demonstrate these concepts using examples from previous robot scientists, and also from Genesis: a next generation robot scientist designed for research in systems biology, comprising a micro-fluidic system with 1000 computer-controlled micro-bioreactors and interpretable models based in controlled vocabularies and logic
Author
Alexander Gower
University of Gothenburg
Chalmers, Computer Science and Engineering (Chalmers), Data Science and AI
Konstantin Korovin
Chalmers, Life Sciences, Systems and Synthetic Biology
Daniel Brunnsåker
Chalmers, Computer Science and Engineering (Chalmers), Data Science and AI
University of Gothenburg
Filip Kronström
Chalmers, Computer Science and Engineering (Chalmers), Data Science and AI
University of Gothenburg
Gabriel Reder
Chalmers, Computer Science and Engineering (Chalmers), Data Science and AI
University of Gothenburg
Ievgeniia Tiukova
Chalmers, Life Sciences, Infrastructures
Ronald S. Reiserer
Vanderbilt University
Ross King
Chalmers, Computer Science and Engineering (Chalmers), Data Science and AI
University of Gothenburg
Subject Categories (SSIF 2025)
Robotics and automation
Computer Sciences
DOI
10.48550/arXiv.2406.17835