King's College London

King’s Autonomous Labs is a cross-university research initiative developing new approaches to automated and autonomous scientific discovery.

KAL brings together artificial intelligence, laboratory automation, robotics, sensing and data to develop self-driving laboratories. These are connected experimental systems that can perform experiments, analyse results and use emerging evidence to inform what happens next.

A self-driving laboratory is not a laboratory without people. Researchers remain responsible for defining the scientific questions, objectives, boundaries and safeguards. By combining their expertise with automation and AI, KAL aims to make experimentation more adaptive, reproducible and efficient.

Our first grand challenge: living systems

KAL’s first grand challenge is living systems, including cells, organoids, tissues and microbial systems.

Experiments involving living systems can be lengthy, variable and sensitive to their surroundings. They can also generate multiple forms of evidence. Automated systems therefore need to distinguish biological change from technical failure, recognise uncertainty and identify when human judgement is required.

This makes living systems both an important scientific opportunity and a demanding test of responsible laboratory autonomy. The methods, infrastructure and approaches developed through this work could also support research in other experimental disciplines.

Pump-Priming Funding

The KAL Pump-Priming call will support innovative projects using AI, automation, robotics and data-driven approaches to advance autonomous scientific discovery in living systems. Funding of approximately £10k–£30k is available for projects of up to six months, supporting pilot data, proofs of concept, demonstrators, new capabilities and interdisciplinary collaborations that can underpin future external funding applications.

The call is open to King’s staff, subject to the eligibility criteria in the call guidance. Applications should be submitted by 12 noon on Thursday 15 October 2026.

Full guidance and the application form are available via this internal King's-only link.

Join the KAL community

KAL brings together researchers and technical professionals from across King’s who are developing automated and autonomous approaches to scientific discovery.

The KAL interest group provides opportunities to connect with colleagues across disciplines, share scientific challenges and technical capabilities, and hear about funding, events, demonstrators and potential collaborations.

The group is open to colleagues working in experimental science, laboratory automation, robotics, sensing, imaging, AI, data, statistics, research software and engineering. It also welcomes expertise in areas such as reproducibility, ethics, cyber security, governance, provenance and responsible innovation.

Join the KAL interest group.

KAL in action

King’s already has experimental platforms demonstrating complementary parts of the self-driving laboratory pathway. These examples show how automation, sensing, imaging, data and AI can be connected to make experimentation more reproducible, responsive and sustainable.

The video shows a Biomanufacturing system developed by Dr Miao Guo.

The photos show Dr Andrea Serio's VISIBLE system.

Digital infrastructure for self-driving laboratories

KAL is supported by King’s Advanced Research Computing (ARC) capabilities. These provide the compute, secure digital environments, research data infrastructure and technical expertise needed to process imaging and sensor data, develop and deploy AI models, run simulations and connect digital decision-making with laboratory systems.

KAL will work with ARC colleagues to ensure that projects use appropriate, secure and proportionate computing environments, with attention to data governance, software sustainability, reproducibility and operational resilience.

King's Autonomous Labs is an initiative that sits alongside King’s wider Doing Science Well with AI programme led from the King’s Institute for Artificial Intelligence, which asks what happens when AI becomes part of the research process itself.