Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning the Latent Space of Robot Dynamics for Cutting Interaction
  Inference   Utilization of latent space to capture a lower-dimensional representation of
a complex dynamics model is explored in this work. The targeted application is
of a robotic manipulator executing a complex environment interaction task, in
particular, cutting a wooden object. We train two flavours of Variational
Autoencoders---standard and Vector-Quantised---to learn the latent space which
is then used to infer certain properties of the cutting operation, such as
whether the robot is cutting or not, as well as, material and geometry of the
object being cut. The two VAE models are evaluated with reconstruction,
prediction and a combined reconstruction/prediction decoders. The results
demonstrate the expressiveness of the latent space for robotic interaction
inference and the competitive prediction performance against recurrent neural
networks.

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The categories of this paper are: hep-ph
14
The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an
19
The categories of this paper are: eess
24
The categories of this paper are: dg-ga
37
The categories of this paper are: supr-con
2
The categories of this paper are: math
11
The categories of this paper are: hep-lat
36
The categories of this paper are: cmp-lg
3
The categories of this paper are: physics
29
The categories of this paper are: funct-an
23
The categories of this paper are: solv-int
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The categories of this paper are: hep-th
13
The categories of this paper are: quant-ph
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The categories of this paper are: nlin
30
The categories of this paper are: q-alg