  It is important for daily life support robots to detect changes in their
environment and perform tasks. In the field of anomaly detection in computer
vision, probabilistic and deep learning methods have been used to calculate the
image distance. These methods calculate distances by focusing on image pixels.
In contrast, this study aims to detect semantic changes in the daily life
environment using the current development of large-scale vision-language
models. Using its Visual Question Answering (VQA) model, we propose a method to
detect semantic changes by applying multiple questions to a reference image and
a current image and obtaining answers in the form of sentences. Unlike deep
learning-based methods in anomaly detection, this method does not require any
training or fine-tuning, is not affected by noise, and is sensitive to semantic
state changes in the real world. In our experiments, we demonstrated the
effectiveness of this method by applying it to a patrol task in a real-life
environment using a mobile robot, Fetch Mobile Manipulator. In the future, it
may be possible to add explanatory power to changes in the daily life
environment through spoken language.
