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In recent years, a number of models that learn the relations between visionand language from large datasets have been released. These models perform avariety of tasks, such as answering questions about images, retrievingsentences that best correspond to images, and finding regions in images thatcorrespond to phrases. Although there are some examples, the connection betweenthese pre-trained vision-language models and robotics is still weak. If theyare directly connected to robot motions, they lose their versatility due to theembodiment of the robot and the difficulty of data collection, and becomeinapplicable to a wide range of bodies and situations. Therefore, in thisstudy, we categorize and summarize the methods to utilize the pre-trainedvision-language models flexibly and easily in a way that the robot canunderstand, without directly connecting them to robot motions. We discuss howto use these models for robot motion selection and motion planning withoutre-training the models. We consider five types of methods to extractinformation understandable for robots, and show the results of staterecognition, object recognition, affordance recognition, relation recognition,and anomaly detection based on the combination of these five methods. We expectthat this study will add flexibility and ease-of-use, as well as newapplications, to the recognition behavior of existing robots.

Robotic Applications of Pre-Trained Vision-Language Models to Various Recognition Behaviors