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

Task success can mask unsafe execution in scientific robotics. On the benchtop, agents must remain safe throughout a rollout rather than merely reach a final goal, because small pose, force, or tilt errors can cause irreversible spillage or equipment damage. Yet prevailing benchmarks emphasize reversible, high-tolerance manipulation, and imitation-trained policies receive no interactive signal to correct execution drift. We introduce SafeLab, a fluid-aware generative bench-Proceedings of the 43 rd International Conference on Machine Learning, Seoul, South Korea. PMLR 306, 2026. Copyright 2026 by the author(s). mark that turns this trajectory-level requirement into an evaluation protocol through verified task synthesis, teleoperation-free expert demonstrations, and dense safety-aware reinforcement learning (RL) feedback. The benchmark provides 64 tasks across 9 manipulation categories, 63 calibrated laboratory assets, and 6,400 expert trajectories. Evaluating five representative policies on SafeLab shows that task success often coexists with safety violations: Safe Success Rate (SSR) trails Success Rate (SR) by more than 30 percentage points in every evaluation domain (liquid handling, instrument actuation, and glassware rearrangement). Bounded residual RL learns execution-level corrections on frozen base policies, improving simulated SSR by 33.1 to 43.0 percentage points, and 50 open-loop physical replays match simulated safety labels in 86% of cases. SafeLab thus treats laboratory readiness as safe execution rather than goal completion alone, and provides a scalable benchmark for screen-