Scientists at Argonne National Laboratory are developing AI-powered robotic assistants that could learn laboratory procedures directly from human researchers and eventually help automate complex scientific experiments, El.kz cites Interesting Engineering.
The project, called RoSA, short for Robot Scientific Assistant for Accelerating Experimental Workflows, aims to create robots capable of working alongside scientists in real laboratory environments while adapting to changing conditions and different types of experiments.
Researchers say the effort is part of the U.S. Department of Energy’s Genesis Mission, a national initiative focused on using artificial intelligence, quantum computing, and supercomputers to speed up scientific discovery and double American research productivity within the next decade.
Instead of programming every action manually, the Argonne team plans to train robots by observing scientists as they perform experiments. Researchers will wear sensors while carrying out laboratory tasks, allowing the system to capture movements, workflows, and decision-making patterns that robots can later imitate.
“Robots with fine motor skills already exist but using them safely and effectively in real laboratories is still very challenging,” said Nicola Ferrier, senior computer scientist at Argonne in a release. “Our approach starts by learning directly from expert scientists as they do their work.”
Robots learn experiments
The recorded data will be used to develop AI models capable of teaching robots how scientific procedures are correctly performed. The researchers believe this learning-based approach could help robots adapt to dynamic lab conditions without requiring constant reprogramming.
Ferrier is leading the robotics and computer vision side of the project, while computational scientist Arvind Ramanathan is contributing expertise in autonomous laboratories and AI-driven decision-making systems.
According to the team, the project will also classify common laboratory tasks based on their complexity and precision requirements. Different robotic systems will then be matched to the most suitable jobs.
The researchers are exploring the use of fixed-base robotic arms, humanoid robots, and hybrid robotic systems that combine mobility with stationary precision. Before deployment in real laboratories, the systems will first be tested in virtual simulation environments.
“Our main goal is to strengthen the basic robotics and computing tools needed so that large-scale, automated robotic systems can carry out experiments faster and more reliably,” Ferrier said.
Faster science through AI
The project is also expected to support another DOE-backed initiative called OPAL, or Orchestrated Platform for Autonomous Laboratories, which focuses on creating networks of self-driving laboratories capable of adapting and learning independently.
“In OPAL, dexterous robotics – which are well coordinated and nimble – are being planned for executing biological experiments,” Ramanathan said. “By integrating AI-driven decision-making with advanced robotics, we aim to create systems that can accelerate discovery across a wide range of scientific disciplines.”
Researchers say robotic scientific assistants could eventually handle repetitive or hazardous laboratory work while improving the speed and consistency of experiments.
The Argonne team hopes to demonstrate a fivefold increase in task efficiency within the next year as development progresses.
“Within the next year we hope to show a fivefold improvement in how efficiently these tasks can be completed,” Ferrier said. “In the long term, we envision robot scientific assistants that can work with existing laboratory equipment, making complex experiments both safer and more efficient. RoSA is a key step toward that future.”
