Self-Driving Labs: AI and Robotics Accelerate Scientific Discovery

AI and robotics are set to reset how labs discover

A new viewpoint in Science Robotics outlines how globally connected, autonomous laboratories could change how science gets done. The core idea: link AI-driven experiment design with robotic execution and machine learning analysis to compress discovery cycles from years to months.

Researchers from MBZUAI, TUM, the University of Toronto, and other institutions argue for human-centered automation. As MBZUAI's Sami Haddadin notes, when scientists stay in the loop, automation works as a partner that speeds progress across biology, chemistry, biophysics, and materials science.

The viewpoint was published on 24 September 2025.

From manual runs to closed-loop discovery

Closed-loop labs use AI to plan experiments, robots to execute them, and algorithms to analyze results and refine the next iteration. This continuous cycle raises throughput and reduces trial-and-error overhead.

Beyond throughput, closed loops enable systematic exploration of large design spaces. That means more actionable data per unit time and tighter feedback between hypotheses and results.

What next-generation labs look like

• Human-machine partnership: Keep scientific intent, oversight, and interpretation with experts; let automation handle precision and scale.
• Modular, flexible systems: Plug-and-play instruments, standard interfaces, and reconfigurable workflows.
• AI-driven planning: Active learning and Bayesian optimization to guide experiments and cut waste.
• Digital twins: Simulated lab and process models to reduce errors, tune parameters, and preview outcomes.
• Vendor-neutral standards: Interoperability by default to enable scale across sites and suppliers.
• Responsible automation: Clear ethics, safety, and regulatory alignment from day one.

The real bottleneck: data and interoperability

AI needs high-quality, machine-actionable data. Many labs still lack FAIR data practices-information that is Findable, Accessible, Interoperable, and Reusable. Adopting the FAIR principles is foundational.

Standards such as SiLA, shared software platforms, and participation in global consortia (e.g., the Acceleration Consortium) are critical. They reduce integration friction and open paths to "science as a service," where researchers can run experiments remotely on cloud-connected labs.

What you can implement this quarter

• Audit data readiness: Map your data lifecycle against FAIR. Add persistent identifiers, versioning, and standardized metadata.
• Standardize interfaces: Pilot vendor-neutral protocols (e.g., SiLA) on one instrument cluster and document the integration playbook.
• Automate the routine: Start with high-variance or high-volume tasks-pipetting, plate handling, simple reaction runs-then expand.
• Close the loop on a narrow task: Pair an optimizer (Bayesian/active learning) with a robot cell to tune one process parameter end-to-end.
• Stand up a digital twin: Create a lightweight simulation of a workflow to test schedules, constraints, and error handling before deployment.
• Establish governance: Define data ownership, access control, audit trails, and an ethics review for autonomous operations.
• Upskill your team: Train scientists in automation-aware experimental design, ML basics, and failure modes.

Why this matters for research leaders and funders

Cloud-enabled, interoperable labs lower barriers to participation. Teams without local infrastructure can still run validated workflows at scale.

Standardized data and automation increase reproducibility and accelerate meta-analysis across sites. Investments in shared infrastructure and training return compounding benefits over time.

Today's proof points

Labs already automate routine tasks such as ISO-standard pipetting in cell culture and use AI to optimize complex processes like spray coating. These examples show how automation supports both repetitive precision and high-dimensional optimization, freeing scientists to focus on ideas, models, and interpretation.

Outlook

The path ahead is clear: human-centered, AI-guided, robot-executed science, connected across institutions and time zones. As standards mature and data quality improves, self-driving labs will not replace scientists-they will expand what small teams can accomplish.

Further reading and skills

• Curated AI courses by job role for researchers, data scientists, and lab managers implementing AI-driven workflows.