Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Reducing Tactile Sim2Real Domain Gaps via Deep Texture Generation
  Networks   Recently simulation methods have been developed for optical tactile sensors
to enable the Sim2Real learning, i.e., firstly training models in simulation
before deploying them on the real robot. However, some artefacts in the real
objects are unpredictable, such as imperfections caused by fabrication
processes, or scratches by the natural wear and tear, and thus cannot be
represented in the simulation, resulting in a significant gap between the
simulated and real tactile images. To address this Sim2Real gap, we propose a
novel texture generation network that maps the simulated images into
photorealistic tactile images that resemble a real sensor contacting a real
imperfect object. Each simulated tactile image is first divided into two types
of regions: areas that are in contact with the object and areas that are not.
The former is applied with generated textures learned from real textures in the
real tactile images, whereas the latter maintains its appearance as when the
sensor is not in contact with any object. This makes sure that the artefacts
are only applied to the deformed regions of the sensor. Our extensive
experiments show that the proposed texture generation network can generate
these realistic artefacts on the deformed regions of the sensor, while avoiding
leaking the textures into areas of no contact. Quantitative experiments further
reveal that when using the adapted images generated by our proposed network for
a Sim2Real classification task, the drop in accuracy caused by the Sim2Real gap
is reduced from 38.43% to merely 0.81%. As such, this work has the potential to
accelerate the Sim2Real learning for robotic tasks requiring tactile sensing.

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The categories of this paper are: dg-ga
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The categories of this paper are: atom-ph
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The categories of this paper are: physics
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The categories of this paper are: acc-phys
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The categories of this paper are: bayes-an
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The categories of this paper are: chao-dyn
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The categories of this paper are: math
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The categories of this paper are: comp-gas
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The categories of this paper are: hep-ex
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The categories of this paper are: nucl-ex
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The categories of this paper are: supr-con
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The categories of this paper are: eess
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The categories of this paper are: cmp-lg
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The categories of this paper are: nucl-th
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The categories of this paper are: funct-an
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The categories of this paper are: mtrl-th