Learning Social Affordance for Human-Robot Interaction
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 3454-3460.
Abstract
In this paper, we present an approach for robot learning of social affordance from human activity videos. We consider the problem in the context of human-robot interaction: our approach learns structural representations of human-human (and human-object-human) interactions, describing how body-parts of each agent move with respect to each other and what spatial relations they should maintain to complete each sub-event (i.e., sub-goal). This enables the robot to infer its own movement in reaction to the human body motion, allowing it to naturally replicate such interactions.
@inproceedings{ijcai2016p488,
title = {Learning Social Affordance for Human-Robot Interaction},
author = {Shu, Tianmin and Ryoo, Michael. S. and Zhu, Song-Chun},
booktitle = {Proceedings of the Twenty-Fifth International Joint Conference on
Artificial Intelligence, {IJCAI-16}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Subbarao Kambhampati},
pages = {3454--3460},
year = {2016},
month = {7},
url = {https://ijcai.org/proceedings/2016/488},
}
TY - CPAPER TI - Learning Social Affordance for Human-Robot Interaction AU - Shu, Tianmin AU - Ryoo, Michael. S. AU - Zhu, Song-Chun T2 - Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2016 SP - 3454 EP - 3460 UR - https://ijcai.org/proceedings/2016/488 ER -Copied
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