  State recognition of objects and environment in robots has been conducted in
various ways. In most cases, this is executed by processing point clouds,
learning images with annotations, and using specialized sensors. In contrast,
in this study, we propose a state recognition method that applies Visual
Question Answering (VQA) in a Pre-Trained Vision-Language Model (PTVLM) trained
from a large-scale dataset. By using VQA, it is possible to intuitively
describe robotic state recognition in the spoken language. On the other hand,
there are various possible ways to ask about the same event, and the
performance of state recognition differs depending on the question. Therefore,
in order to improve the performance of state recognition using VQA, we search
for an appropriate combination of questions using a genetic algorithm. We show
that our system can recognize not only the open/closed of a refrigerator door
and the on/off of a display, but also the open/closed of a transparent door and
the state of water, which have been difficult to recognize.
