Online Learning of Object Representations by Appearance Space Feature Alignment
Sören Pirk, Mohi Khansari, Yunfei Bai, Corey Lynch, Pierre Sermanet
Abstract
We propose a self-supervised approach for learning representations of objects from monocular videos and demonstrate it is particularly useful for robotics. The main contributions of this paper are: 1) a self-supervised model called Object-Contrastive Network (OCN) that can discover and disentangle object attributes from video without using any labels; 2) we leverage self-supervision for online adaptation: the longer our online model looks at objects in a video, the lower the object identification error, while the offline baseline remains with a large fixed error; 3) we show the usefulness of our approach for a robotic pointing task; a robot can point to objects similar to the one presented in front of it. Videos illustrating online object adaptation and robotic pointing are provided as supplementary material.
BibTeX
@inproceedings{icra2020_onlinelearningof,
title = {Online Learning of Object Representations by Appearance Space Feature Alignment},
author = {Sören Pirk and Mohi Khansari and Yunfei Bai and Corey Lynch and Pierre Sermanet},
booktitle = {ICRA 2020},
year = {2020}
}