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Learning to Order Objects Using Haptic and Proprioceptive Exploratory Behaviors.
Jivko
Sinapov, Priyanka Khante, Maxwell Svetlik, and Peter Stone.
In Proceedings
of the 25th International Joint Conference on Artificial Intelligence (IJCAI), July 2016.
This paper proposes a novel framework that enablesa robot to learn ordinal object relations. While mostrelated work focuses on classifying objects into dis-crete categories, such approaches cannot learn ob-ject properties (e.g., weight, height, size, etc.) thatare context-specific and relative to other objects.To address this problem, we propose that a robotshould learn to order objects based on ordinal ob-ject relations. In our experiments, the robot ex-plored a set of 32 objects that can be ordered bythree properties: height, weight, and width. Next,the robot used unsupervised learning to discovermultiple ways that the objects can be ordered basedon the haptic and proprioceptive perceptions de-tected while exploring the objects. Following, therobot’s model was presented with labeled object se-ries, allowing it to ground the three ordinal relationsin terms of how similar they are to the orders dis-covered during the unsupervised stage. Finally, thegrounded models were used to recognize whethernew object series were ordered by any of the threeproperties as well as to correctly insert additionalobjects into an existing series.
@InProceedings{IJCAI16-sinapov,
author = {Jivko Sinapov and Priyanka Khante and Maxwell Svetlik and Peter Stone},
title = {Learning to Order Objects Using Haptic and Proprioceptive Exploratory Behaviors},
booktitle = {Proceedings of the 25th International Joint Conference on Artificial
Intelligence (IJCAI)},
location = {New York City, USA},
month = {July},
year = {2016},
abstract = {This paper proposes a novel framework that enables
a robot to learn ordinal object relations. While most
related work focuses on classifying objects into dis-
crete categories, such approaches cannot learn ob-
ject properties (e.g., weight, height, size, etc.) that
are context-specific and relative to other objects.
To address this problem, we propose that a robot
should learn to order objects based on ordinal ob-
ject relations. In our experiments, the robot ex-
plored a set of 32 objects that can be ordered by
three properties: height, weight, and width. Next,
the robot used unsupervised learning to discover
multiple ways that the objects can be ordered based
on the haptic and proprioceptive perceptions de-
tected while exploring the objects. Following, the
robot’s model was presented with labeled object se-
ries, allowing it to ground the three ordinal relations
in terms of how similar they are to the orders dis-
covered during the unsupervised stage. Finally, the
grounded models were used to recognize whether
new object series were ordered by any of the three
properties as well as to correctly insert additional
objects into an existing series.
},
}
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