MicroBio-Bench: Can Vision Language Action Models Work in Microbiology Labs?

Vision–language–action (VLA) models offer a way to automate laboratory procedures that require precise manipulation and decisions about when a tool can still be used. In microbiology, a disposable tool whose working end touches a prohibited surface must be discarded before further sample contact, even if it remains correctly positioned for use. We introduce MicroBio-Bench, a simulation environment and benchmark for evaluating how VLA models compose laboratory operations and respond when contamination changes the required action. The suite connects 58 tasks across atomic actions, local operations, and multi-operation tasks, and tracks tool contacts to determine when disposal is required. A contamination-response study tests whether policies learn disposal, retain normal tool use, and generalize to new contact sites. We evaluate three models over 12,750 simulation episodes, complemented by hardware tests. Atomic-action success averages 88.8–97.3%, yet mean multi-operation task success peaks at 26.5%. Response demonstrations yield 100% disposal success for loops and 86–92% for spreaders at seen sites, while unseen-site success falls to 0–20% and 0–18%, respectively. These findings identify workflow composition and generalization of contamination response as key challenges for VLA models in microbiology labs.