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@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.
  },
}