Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Exploiting Embodied Simulation to Detect Novel Object Classes Through
  Interaction   In this paper we present a novel method for a naive agent to detect novel
objects it encounters in an interaction. We train a reinforcement learning
policy on a stacking task given a known object type, and then observe the
results of the agent attempting to stack various other objects based on the
same trained policy. By extracting embedding vectors from a convolutional
neural net trained over the results of the aforementioned stacking play, we can
determine the similarity of a given object to known object types, and determine
if the given object is likely dissimilar enough to the known types to be
considered a novel class of object. We present the results of this method on
two datasets gathered using two different policies and demonstrate what
information the agent needs to extract from its environment to make these
novelty judgments.

11
The categories of this paper are: hep-lat
10
The categories of this paper are: q-bio
29
The categories of this paper are: funct-an
19
The categories of this paper are: eess
31
The categories of this paper are: ao-sci
13
The categories of this paper are: quant-ph
26
The categories of this paper are: bayes-an
16
The categories of this paper are: stat
8
The categories of this paper are: hep-ex
37
The categories of this paper are: supr-con
5
The categories of this paper are: gr-qc
17
The categories of this paper are: q-fin
2
The categories of this paper are: math
34
The categories of this paper are: chem-ph
24
The categories of this paper are: dg-ga
15
The categories of this paper are: nucl-ex