  When deploying a robot to a new task, one often has to train it to detect
novel objects, which is time-consuming and labor-intensive. We present TAILOR
-- a method and system for object registration with active and incremental
learning. When instructed by a human teacher to register an object, TAILOR is
able to automatically select viewpoints to capture informative images by
actively exploring viewpoints, and employs a fast incremental learning
algorithm to learn new objects without potential forgetting of previously
learned objects. We demonstrate the effectiveness of our method with a KUKA
robot to learn novel objects used in a real-world gearbox assembly task through
natural interactions.
