Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DeepSym: Deep Symbol Generation and Rule Learning from Unsupervised
  Continuous Robot Interaction for Planning   Autonomous discovery of discrete symbols and rules from continuous
interaction experience is a crucial building block of robot AI, but remains a
challenging problem. Solving it will overcome the limitations in scalability,
flexibility, and robustness of manually-designed symbols and rules, and will
constitute a substantial advance towards autonomous robots that can learn and
reason at abstract levels in open-ended environments. Towards this goal, we
propose a novel and general method that finds action-grounded, discrete object
and effect categories and builds probabilistic rules over them that can be used
in complex action planning. Our robot interacts with single and multiple
objects using a given action repertoire and observes the effects created in the
environment. In order to form action-grounded object, effect, and relational
categories, we employ a binarized bottleneck layer of a predictive, deep
encoder-decoder network that takes as input the image of the scene and the
action applied, and generates the resulting object displacements in the scene
(action effects) in pixel coordinates. The binary latent vector represents a
learned, action-driven categorization of objects. To distill the knowledge
represented by the neural network into rules useful for symbolic reasoning, we
train a decision tree to reproduce its decoder function. From its branches we
extract probabilistic rules and represent them in PPDDL, allowing off-the-shelf
planners to operate on the robot's sensorimotor experience. Our system is
verified in a physics-based 3d simulation environment where a robot arm-hand
system learned symbols that can be interpreted as 'rollable', 'insertable',
'larger-than' from its push and stack actions; and generated effective plans to
achieve goals such as building towers from given cubes, balls, and cups using
off-the-shelf probabilistic planners.

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The categories of this paper are: atom-ph
8
The categories of this paper are: hep-ex
5
The categories of this paper are: gr-qc
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The categories of this paper are: adap-org
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The categories of this paper are: math-ph
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The categories of this paper are: q-alg
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The categories of this paper are: nucl-ex
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The categories of this paper are: patt-sol
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The categories of this paper are: nucl-th
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The categories of this paper are: eess
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The categories of this paper are: stat
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The categories of this paper are: cond-mat
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The categories of this paper are: mtrl-th
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The categories of this paper are: dg-ga
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The categories of this paper are: chem-ph
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The categories of this paper are: cmp-lg