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When deploying a robot to a new task, one often has to train it to detectnovel objects, which is time-consuming and labor-intensive. We present TAILOR-- a method and system for object registration with active and incrementallearning. When instructed by a human teacher to register an object, TAILOR isable to automatically select viewpoints to capture informative images byactively exploring viewpoints, and employs a fast incremental learningalgorithm to learn new objects without potential forgetting of previouslylearned objects. We demonstrate the effectiveness of our method with a KUKArobot to learn novel objects used in a real-world gearbox assembly task throughnatural interactions.

TAILOR: Teaching with Active and Incremental Learning for Object Registration