  Recognition of the current state is indispensable for the operation of a
robot. There are various states to be recognized, such as whether an elevator
door is open or closed, whether an object has been grasped correctly, and
whether the TV is turned on or off. Until now, these states have been
recognized by programmatically describing the state of a point cloud or raw
image, by annotating and learning images, by using special sensors, etc. In
contrast to these methods, we apply Visual Question Answering (VQA) from a
Pre-Trained Vision-Language Model (PTVLM) trained on a large-scale dataset, to
such binary state recognition. This idea allows us to intuitively describe
state recognition in language without any re-training, thereby improving the
recognition ability of robots in a simple and general way. We summarize various
techniques in questioning methods and image processing, and clarify their
properties through experiments.
