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Object Recognition With Continual Open Set Domain Adaptation for Home Robot
Object recognition ability is indispensable for robots to act like humans in a home environment. For example, when considering an object searching task, humans can recognize a naturally arranged object previously held in their hands while ignoring never observed objects. Even in such a simple task, we need to deal with three complex problems: domain adaptation, open-set recognition, and continual learning. However, most existing datasets are simplified to focus on one problem and do not measure the object recognition ability for home robots when multiple problems are simultaneously present. In this paper, we propose the COSDA-HR (Continual Open Set Domain Adaptation for Home Robot) dataset that requires dealing with the above three problems simultaneously. The COSDA-HR dataset focuses particularly on the scenario in which naturally arranged objects in a room are recognized by training with handheld objects towards the goal of creating a user-friendly teaching system for home robots. We provide various baselines to address the problems in the COSDA-HR dataset by combining state-of-the-art methods from each research area and analyze the limitations of such simple combinations. We consider that it is necessary to study the methods of handling multiple problems simultaneously instead of solving each problem to realize practical object recognition systems for home robots.