Autonomous Laboratories

Autonomous laboratories are the next step in DRAGGON Lab’s work on automated design-build-test-learn workflows. Machine-readable designs, build plans, experimental metadata, and learning-ready datasets provide the foundation. The research challenge is to connect those digital workflows to an entire physical facility that can plan, execute, monitor, recover, and learn with progressively less human intervention.

From automation to autonomy

Automation makes individual procedures reproducible and executable by machines. Autonomy adds coordination and adaptation across procedures, instruments, samples, and time. An autonomous DBTL facility must decide what can run next, confirm that required materials and equipment are available, preserve sample identity and provenance, respond to changing conditions, and return trustworthy results to the design and learning loop.

Facility orchestration

Facility-level software will coordinate experiment scheduling, inventory and reagent tracking, environmental monitoring, equipment availability, sample storage, and transfers between workcells. It should connect liquid-handling robots, incubators, plate readers, imaging systems, storage devices, and data infrastructure without losing the biological intent or operational history of an experiment.

Humanoid robots in human laboratories

Humanoid robots can provide a physical interoperability layer for laboratories designed around people. They could transport reagents and plates, prepare the decks of liquid-handling robots, load and unload measurement equipment, retrieve and store samples, and perform routine interventions without requiring every instrument or room to be rebuilt around fixed automation.

Human coexistence is a core design constraint. Robots and facility software must maintain spatial awareness, respect access and permission boundaries, preserve sterile and safe operating practices, and leave equipment and experiments in understandable states for the people sharing the laboratory.

Tiered autonomy and recovery

Routine operations and well-characterized, recoverable faults should be handled autonomously through validation, retries, replanning, or alternative equipment. Uncertain, biologically significant, or safety-critical conditions should place affected work in a safe state, preserve evidence, and escalate clearly to a person. Every action and recovery decision should remain auditable.

Research trajectory

The path begins with programmatic DBTL workflows and isolated automated workcells. It then expands to coordinated instrument handoffs, robotic sample logistics, overnight operation, multi-day campaigns, and increasingly capable fault recovery. The long-term objective is to operate useful experimental facilities safely for weeks at a time while producing traceable data that improves the next cycle of design and operation.