2022/05/24 by Xu, Qianli, Gauthier, Nicolas, Liang, Wenyu +6
#Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Robotics (cs.RO)
paper · doi:10.48550/arxiv.2205.11692
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.