Scientific discovery is poised for rapid advancement through advanced robotics and artificial intelligence. Current scientific practices face substantial limitations as manual experimentation remains time-consuming and resource-intensive, while multidisciplinary research demands knowledge integration beyond individual researchers' expertise boundaries. Here, we envision an autonomous generalist scientist (AGS) concept combines agentic AI and embodied robotics to automate the entire research lifecycle. This system could dynamically interact with both physical and virtual environments while facilitating the integration of knowledge across diverse scientific disciplines. By deploying these technologies throughout every research stage -- spanning literature review, hypothesis generation, experimentation, and manuscript writing -- and incorporating internal reflection alongside external feedback, this system aims to significantly reduce the time and resources needed for scientific discovery. Building on the evolution from virtual AI scientists to versatile generalist AI-based robot scientists, AGS promises groundbreaking potential. As these autonomous systems become increasingly integrated into the research process, we hypothesize that scientific discovery might adhere to new scaling laws, potentially shaped by the number and capabilities of these autonomous systems, offering novel perspectives on how knowledge is generated and evolves. The adaptability of embodied robots to extreme environments, paired with the flywheel effect of accumulating scientific knowledge, holds the promise of continually pushing beyond both physical and intellectual frontiers.

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.

If you have been watching the world of artificial intelligence (AI), then you see science fiction come to life before your eyes. Not only are we watching it achieve incredible feats, but we are also seeing them created at an amazing rate. It's just a matter of time before AI becomes a normal functioning member of our society. Those who are resistant to change and new methods are in for a rough time. This is because more and more of our society's occupations will rely on AI in some form or fashion.

Reproducibility of experimental results is a cornerstone of biology research. Today, many of these experiments are done using automated machines such as robots and microfluidic chips However, published reports about the work explain the experimentation method in plain English, which must be interpreted by other groups to reproduce the experiment. Biological protocols give a recipe for a biological experiment. Ideally, we would like these protocols to be specified rigorously and precisely. Once we do that, we are a step away from automation, reproducibility, and also repurposing.

An intelligent robot that works independently 21.5 hours a day is helping scientists at the University of Liverpool with their research using AI, its flexible arm and a customised gripper.

Researchers at the University of Liverpool have built an intelligent, mobile, robotic scientist that can solve a range of research problems. The robot seen here can work almost 24-7, carrying out experiments by itself. The automated scientist – the first of its kind – can make its own decisions about which chemistry experiments to perform next, and has already discovered a new catalyst. With humanoid dimensions, and working in a standard laboratory, it uses instruments much like a human does. Unlike a real person, however, this 400 kg robot has infinite patience, and works for 21.5 hours each day, pausing only to recharge its battery.

A car factory robot, adapted by British scientists to work in a chemistry lab, completed an experiment in three days that would have taken a human months. University of Liverpool researchers reprogrammed the £100,000 autonomous arm, giving it enough intelligence that it can perform experiments without input. The robot is significantly more efficient, able to perform up to 700 experiments in a week - the same number a student might complete over the course of a PhD. It has already made a contribution to the Liverpool lab where it is based, working around the clock to complete 688 different experiments over 172 hours. The developers said the goal was to find a way to'automate the researcher' rather than the tools that scientists use to carry out experiments.

A robot scientist capable of carrying out experiments by itself has made its first discovery, a new study has revealed. Researchers at the University of Liverpool built the intelligent mobile robot chemist in the hope of solving a range of research problems. Similar to a human researcher, the first-of-its-kind robot is able to use standard laboratory equipment with its humanoid limbs, as well as make its own decisions about which experiments to perform. Unlike a human, however, the 400kg machine can think in 10 dimensions and is capable of working for up to 21.5 hours a day – pausing only to recharge its batteries. "Our strategy here was to automate the researcher, rather than the instruments," said Professor Andrew Cooper from the university's Department of Chemistry and Materials Innovation Factory.

Science is exciting in theory, but it can also be dreadfully dull. Some experiments require hundreds or thousands of repetitions or trials -- an excellent opportunity to automate. That's just what MIT scientists have done, creating a robot that performs a certain experiment, observes the results, and plans a follow-up… and has now done so 100,000 times in the year it's been operating. The field of fluid dynamics involves a lot of complex and unpredictable forces, and sometimes the best way to understand them is to repeat things over and over until patterns emerge. One of the observations that needs to be performed is of "vortex-induced vibration," a kind of disturbance that matters a lot to designing ships that travel through water efficiently. It involves close observation of an object moving through water… over, and over, and over.

In a laboratory that overlooks a busy shopping street in Cambridge, Massachusetts, a robot is attempting to create new materials. A robot arm dips a pipette into a dish and transfers a tiny amount of bright liquid into one of many receptacles sitting in front of another machine. When all the samples are ready, the second machine tests their optical properties, and the results are fed to a computer that controls the arm. Software analyzes the results of these experiments, formulates a few hypotheses, and then starts the process over again. The setup, developed by a startup called Kebotix, hints at how machine learning and robotic automation may be poised to revolutionize materials science in coming years.