Authors: Jiaru Bai, Yuchi Zhao, Han Hao, Miroslav Bogdanovic, Yang Cao, Varinia Bernales, Alán Aspuru-Guzik
Organizations: Department of Chemistry, University of Toronto, 80 St. George St., Toronto, ON M5S 3H6, Canada · Vector Institute for Artificial Intelligence, W1140-108 College St., Schwartz Reisman Innovation Campus, Toronto, ON M5G 0C6, Canada · Department of Computer Science, University of Toronto, 40 St George St., Toronto, ON M5S 2E4, Canada · Acceleration Consortium, 700 University Ave., Toronto, ON M7A 2S4, Canada · Department of Materials Science & Engineering, University of Toronto, 184 College St., Toronto, ON M5S 3E4, Canada · Department of Chemical Engineering & Applied Chemistry, University of Toronto, 200 College St., Toronto, ON M5S 3E5, Canada · Institute of Medical Science, 1 King’s College Circle, Medical Sciences Building, Room 2374, Toronto, ON M5S 1A8, Canada · Canadian Institute for Advanced Research (CIFAR), 661 University Ave., Toronto, ON M5G 1M1, Canada · NVIDIA, 431 King St W #6th, Toronto, ON M5V 1K4, Canada
Self-driving laboratories (SDLs) accelerate scientific discovery by automating experimental execution and optimization. AI agents can increasingly propose new experiments, but existing SDLs can only run the procedures they were built for. Adapting them to a new experiment still takes extensive manual engineering, which makes laboratory flexibility the bottleneck for scaling discovery. We introduce the self-building laboratory (SBL), which designs, configures, and operates its own laboratory setup given a high-level scientific goal. SBL starts from whatever already exists, from an empty room to a working SDL, and builds the missing capabilities: the lab layout, its digital twin, robot skills, and experimental workflows. We demonstrate the system working across different robots and setups, scaling from a single workstation to laboratories with several robots working together. We validate SBL's end-to-end capabilities through a real-world quantum-dot synthesis experiment. By shifting the role of human researchers from manually configuring and programming device automation to specifying scientific objectives, SBL makes scientific automation more accessible, flexible, and scalable. Project website: https://self-building-labs.github.io/
Figures & tables
Figure 1: Concept of a self-building laboratory (SBL) . Scientific goals guide the identification of capability gaps, development in a digital twin, and validation in the physical laboratory. Feedback and reuse support continued capability development across workflows, equipment, and entire laboratories. The image combines SDL2 at the Acceleration Consortium, University of Toronto, Canada, with its digital twin built by the SBL and rendered in Isaac Sim in a blue wireframe style.
Aspect
Traditional lab
Self-driving lab
Self-building lab (this work)
Experiment execution
Researcher-led procedures
Automated workflows using integrated capabilities
Workflows using capabilities developed as needed
New capabilities
Developed by researchers
Typically engineered by specialists
Developed and validated by agents
Knowledge retention
Written procedures and researcher experience
Protocols and script libraries tailored for one setup
Reusable skills across laboratories
Table 1: Comparison of laboratory paradigms.
Figure 2: Architecture of the self-building laboratory. The scientific agent translates user goals into experimental plans, while the twin agent constructs a digital twin , develops task-relevant skills, and validates workflows in simulation. The safety agent uses the twin for scenario analysis and generates runtime guardrails through which all physical laboratory commands pass. The scientific agent invokes the physical laboratory through this layer. Physical state feedback supports twin calibration and subsequent simulation, while simulation and physical execution traces support skill distillation and the retention of experience as agent memory. Reusable skills and memories inform subsequent tasks.
Starting point
Already exists
sbl builds
Empty laboratory
N/A
Layout, digital twin, skills, and workflows
Existing manual laboratory
Equipment
Digital twin, skills, and workflows
Automated laboratory requiring reconfiguration
Equipment and some skills
New layout, updated digital twin, and adapted skills
sdl with a new protocol
Equipment and skills
New workflows and any missing skills
Table 2: Starting points for a self-building laboratory. The same process applies in each case; only the set of capabilities that must be built differs.
Figure 3: Building a digital twin of the laboratory. (a) Starting from laboratory images and equipment model numbers when available, the twin agent identifies individual devices, searches product documentation, and retrieves or constructs equipment models with task-relevant geometry, articulation, and control interfaces. (b) These models are assembled into an initial simulation scene and refined using colour and depth observations acquired from multiple robot poses. The agent compares observed and rendered views and iteratively updates equipment poses to align the scene with the physical laboratory.
Figure 4: Scientific tasks drive laboratory skill development. A plate-sealing task drives the development and integration of embodied and device skills. Digital-twin planning and feedback from physical execution guide refinement of the twin and skills, connecting scientific requirements to executable laboratory capabilities.
Figure 5: sbl -coordinated CdS synthesis and optical readout. sbl executed 236 OT-2 commands to prepare a 96-well plate, followed by 19 robotic stages for transfer, sealing, re-grasping, and reader loading. Overlaid video frames and teal arrows illustrate plate handling. The heatmap shows fluorescence intensity at 560 nm after 8 h, with well H8 highlighted. The adjacent plots show the evolution of the H8 emission spectrum and its fluorescence intensity at 560 nm alongside the temperature trace.
Figure 6: Scaling self-building laboratories across robots and workstations. The reconstructed simulation layout (top) integrates a UR3/UR5 solid-handling workstation, a mobile manipulator, and an xArm-based liquid-handling workstation. Given a high-level buffer-preparation goal, SBL generates a coordinated workflow (bottom) in simulation, and also generates task-relevant powder and fluid simulation models for solid and liquid handling.
Department of Chemistry, University of Toronto, 80 St. George St., Toronto, ON M5S 3H6, Canada · Vector Institute for Artificial Intelligence, W1140-108 College St., Schwartz Reisman Innovation Campus, Toronto, ON2026 M5G 0C6, Canada · Acceleration Consortium, 700 University Ave., Toronto, ON M7A 2S4, Canada +11
AX Research Division, Korea Electronics Technology Institute, Republic of Korea · Material Data Division, Korea Institute of Materials Science, Republic of Korea