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Cooking tasks are characterized by large changes in the state of the food,which is one of the major challenges in robot execution of cooking tasks. Inparticular, cooking using a stove to apply heat to the foodstuff causes manyspecial state changes that are not seen in other tasks, making it difficult todesign a recognizer. In this study, we propose a unified method for recognizingchanges in the cooking state of robots by using the vision-language model thatcan discriminate open-vocabulary objects in a time-series manner. We collecteddata on four typical state changes in cooking using a real robot and confirmedthe effectiveness of the proposed method. We also compared the conditions anddiscussed the types of natural language prompts and the image regions that aresuitable for recognizing the state changes.

Recognition of Heat-Induced Food State Changes by Time-Series Use of Vision-Language Model for Cooking Robot