BioLab: Benchmarking Robotic Manipulation in Biology Laboratory from Individual Skills to Experiment SOPs

Robotic automation of biology experiment requires more than executing isolated manipulations. A robot must follow a standard operating procedure (SOP) across multiple instruments while adapting to varied layouts and states produced by earlier steps. Existing laboratory benchmarks assess valuable skills and multi-step tasks, but offer limited evidence about complete SOP-level manipulation. We introduce BioLab, a framework for studying how laboratory skills carry through a full workflow. We develop BioLab-Sim with configurable unified platform and hierarchical annotation & checking for scripted demonstration collection and policy evaluation. Building on this framework, we construct BioLab-Bench by decomposing an SOP into basic laboratory skills (L1), context-dependent operation sequences (L2), and complete biology experiment SOPs (L3). Occurrence-aligned demonstrations and ordered physical checks support action learning and stage-level progress evaluation, while held-out layouts test spatial generalization. We evaluate six vision-language-action policies and find limited reliability even on L1 skills. To study procedural control on longer tasks, we further propose BioLab-HVLA, which combines execution memory and visual progress decisions with leaf-conditioned action control. This hierarchy supports longer workflow execution, although physical checks reveal early manipulation failures and discrepancies between predicted progress and actual completion. We demonstrate that BioLab provides a unified setting for learning, evaluating, and diagnosing the transition from laboratory skills to SOP-level biological manipulation.