A VLM-based Method for Visual Anomaly Detection in Robotic Scientific Laboratories
Journal:
arXiv
Published Date:
Jun 4, 2025
Abstract
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.
