Never-Ending Object Learning

Robots areLearning to recognize objects based on names is a crucial capability for personal robots. Recent recognition methods successfully learn to recognize objects in a train-once- then-test setting. Yet, these methods do not apply readily to robotic settings, where a robot might continuously encounter new objects and new names. In this work, we present a framework for Never-Ending Object Learning (NEOL). Our framework automatically learns to organize object names into a semantic hierarchy using crowdsourcing and background knowledge bases. It then uses the hierarchy to improve the consistency and efficiency of annotating objects. It also adapts information from additional image datasets to learn object classifiers from a very small number of training examples. We present experiments to test the performance of the adaptation method and demonstrate the full system in a never-ending object learning experiment.

This video shows the frames when a new name is added into the hierarchy.


NEOL: Toward Never-Ending Object Learning, Yuyin Sun, Dieter Fox, ICRA 2016


This material is based upon work supported in part by the Intel Science and Technology Center for Pervasive Computing, ONR grant N00014-13-1-0720, and ARO grant W911NF-12-1-0197.