TAILOR: Teaching with Active and Incremental Learning for Object Registration

When deploying a robot to a new task, one often has to train it to detect novel objects, which is time-consuming and laborintensive. 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.

Authors

• Qianli Xu Institute for Infocomm Research (I2R), A*STAR
• Nicolas Gauthier
• Wenyu Liang
• Fen Fang
• Hui Li Tan
• Ying Sun Pennsylvania State University
• Yan Wu Technology Center, Huolala
• Liyuan Li
• Joo-Hwee Lim Institute for Infocomm Research (I2R), A*STAR

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