SafeLab: An Interactive High-Fidelity Benchmark for Embodied Safety in Scientific Robotics

Scientific embodied agents could automate laboratory workflows, but laboratory success is trajectory-level: an agent must remain safe throughout execution, not merely reach a final goal. In benchtop settings a small pose, force, or tilt error can cause irreversible spillage or equipment damage, yet most robot benchmarks evaluate reversible, high-tolerance manipulation and imitation-trained policies receive no recovery signal for execution drift. We introduce SafeLab, a generative simulation benchmark that couples a verified generative engine, an automated expert for teleoperation-free demonstrations, and a safety-aware RL interface, with 63 calibrated laboratory assets, 64 tasks across 9 manipulation categories, and 6,400 expert trajectories. Across state-of-the-art policies, it reveals unsafe-but-successful trajectories—large gaps between task success and Safe Success Rate—most acutely in liquid handling, force-limited actuation, and bimanual glassware rearrangement. Bounded residual RL then learns execution-level corrections that raise the simulated Safe Success Rate by up to 43.0 percentage points without retraining the base policy. Finally, 50 open-loop physical replays show 86% agreement between simulated and observed safety outcomes, supporting SafeLab as a scalable platform for screening and training safer laboratory agents.