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Data · dataset · 2026

Vision-Language Agents for Active Perception in Optics Laboratories

Listed in ZivaHub and Deakin Research Online and DMU Figshare — shown once because both records carry DOI 10.48550/arxiv.2609.32918

Vision-language models (VLMs) are increasingly being used in scientific workflows, but their ability as agents to directly control laboratory experiments from visual feedback remains underexplored.

Description

This capability is important because many laboratory tasks do not naturally provide dense, pre-defined numerical objectives: informative signals can be sparse, intermittent, or visually ambiguous. A more general laboratory agent should instead be able to interpret visual observations, take actions to acquire useful feedback, and adapt its behavior based on the consequences of those actions.

We study whether general-purpose VLMs can perform this kind of closed-loop scientific control using experimental optics as a testbed. We evaluate agents on three experimental systems that isolate distinct capabilities: a Michelson interferometer, a two-mirror cavity, and a four-mirror optical relay. The agents observe camera images, directly issue actuator and measurement commands, and retain their interaction history without receiving an engineered scalar objective during control.

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Across these experiments and matched simulations, we find that, given task-specific natural-language guidance, VLMs can estimate actuator-response relationships, resolve ambiguous observations through intervention, and actively create informative visual feedback when signals are sparse. These results suggest that pretrained multimodal models can serve as important decision-making agents within the experimental loop. Our work also establishes optics as a physically grounded testbed for visual reasoning and active perception in scientific agents.

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Inferred from text
Artificial intelligence 69% · Image 65%
Provenance · 3 source records, 16 field assertions
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ZivaHuboai:figshare.com:article/3403360811 d agoJSON v1
Deakin Research Onlineoai:figshare.com:article/3403360811 d agoJSON v1
DMU Figshareoai:figshare.com:article/3403360811 d agoJSON v1
FieldAssertionExtractorEvidence
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