A VLM-based Method for Visual Anomaly Detection in Robotic Scientific Laboratories
This work addresses process anomaly detection for robotic scientific laboratories, providing a data-driven foundation and evaluation framework, but it is incremental as it adapts existing vision-language models to a specific domain.
The paper tackles visual anomaly detection in robotic scientific laboratories by proposing a VLM-based visual reasoning approach with four prompt configurations, showing that detection accuracy improves with more contextual information in experiments on two models.
In robot scientific laboratories, visual anomaly detection is important for the timely identification and resolution of potential faults or deviations. It has become a key factor in ensuring the stability and safety of experimental processes. To address this challenge, this paper proposes a VLM-based visual reasoning approach that supports different levels of supervision through four progressively informative prompt configurations. To systematically evaluate its effectiveness, we construct a visual benchmark tailored for process anomaly detection in scientific workflows. Experiments on two representative vision-language models show that detection accuracy improves as more contextual information is provided, confirming the effectiveness and adaptability of the proposed reasoning approach for process anomaly detection in scientific workflows. Furthermore, real-world validations at selected experimental steps confirm that first-person visual observation can effectively identify process-level anomalies. This work provides both a data-driven foundation and an evaluation framework for vision anomaly detection in scientific experiment workflows.