Self-driving laboratories made auditable with unified safe control framework
Making Self-Driving Laboratories Auditable:A Unified Framework for Hybrid Actions,Measurements, and Safe Execution
RoboticsArtificial Intelligence
Summary
Self-driving laboratories automatically plan and run experiments using robots and machines, but controlling different devices safely and tracking what they do is hard. The authors propose a new system that coordinates robots and instruments while keeping detailed logs of every action and measurement. Their framework also checks in real time to ensure safety rules are followed and measures how well experiments perform and recover from problems. This approach helps make automated labs easier to monitor, understand, and trust.
What this means in practice
- •For laboratory automation engineers: Coordinate and safely control diverse robotic devices and instruments while capturing detailed experiment logs for audit and troubleshooting in automated labs.
- •For industrial process control teams: Implement runtime safety checks and traceable execution for complex workflows that combine continuous machine movements with discrete instrument commands.
Authors
Zihan Zhang, Zeyu Wu, Xin Zhang, Mingyu Luo, Haohui Que, Tong Zhu
Abstract
Self-driving laboratories (SDLs) are laboratory systems that couple experimental planning, automated execution, and measurement feedback in a closed loop. Integrating flexible robot control with the typed application programming interfaces (APIs) and continuous measurements exposed by laboratory instruments remains an execution-interface challenge. To address this challenge, we propose a traceable framework comprising four components: LAM (Lab Action Model), OpenSDL, Safe-SDL, and L3-Bench. LAM is a proposed measurement-conditioned policy architecture that extends vision-language-action paradigms to generate both continuous robot trajectories and typed instrument API calls. OpenSDL is an execution substrate with an initial control implementation, designed to map role-level operations to heterogeneous devices, coordinate execution, and record timestamped causal events. The proposed Safe-SDL runtime gate combines pre-dispatch schema and resource checks with signal temporal logic monitoring during execution, including extended waiting periods. L3-Bench (Level-3 Laboratory Benchmark) specifies an episode-level evaluation protocol covering physical outcomes, timing, reproducibility, safety violations, and fault recovery. Together, these components provide an end-to-end design for linking hybrid action generation, monitored device execution, measurement feedback, and experimental evaluation, addressing coordination and traceability challenges in SDL workflows.