IEEE Robotics & Automation Magazine - June 2020 - 66

Optimal Deep Learning
for Robot Touch
Training Accurate Pose Models
of 3D Surfaces and Edges

©ISTOCKPHOTO.COM/GMAST3R

By Nathan F. Lepora and John Lloyd

T

his article illustrates the application
of deep learning to robot touch by
considering a basic yet fundamental
capability : estimating the
relative pose of part of an
object in contact with a tactile sensor.
We begin by surveying deep learning
applied to tactile robotics, focusing on
optical tactile sensors, which help to
link touch and deep learning for
vision. We then show how deep
learning can be used to train accurate
pose models of 3D surfaces and edges that
are insensitive to nuisance variables, such as
motion-dependent shear. This involves including
representative motions as unlabeled perturbations of
the training data and using Bayesian optimization of
the network and training hyperparameters to find the
most accurate models. Accurate estimation of the pose
from touch will enable robots to safely and precisely
control their physical interactions, facilitating
a wide range of object exploration and
manipulation tasks.
The Importance of Touch
Our primary human senses of vision, audition, and touch enable us to interact with a
complex and ever-changing environment.
Vision and audition are distal senses with
which we reason about and plan our interactions.
In contrast, touch is a proximal sense that enables us
to interact directly with our surroundings, either to avoid
harm or to explore and manipulate nearby objects. It has
Digital Object Identifier 10.1109/MRA.2020.2979658
Date of current version: 7 April 2020

66

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IEEE ROBOTICS & AUTOMATION MAGAZINE

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JUNE 2020

become something of a
cliché to remark that if
we want robots to interact in a useful way with
our world, they will need
versions of these three
senses and the intelligence
to use them effectively.
However, there is a huge
disparity between the research
effort invested in applying modern artificial intelligence (AI) to distal senses compared with proximal senses. For vision, there has been an explosion of interest,
accounting for many tens of thousands of research papers.
This has not been the case for touch, where there has been a
more gradual increase to roughly 50 published studies. Only
a handful of labs worldwide routinely apply deep learning to
tactile sensing, and fewer still apply that expertise to control
physically interactive robots.
One potential explanation for this disparity is the degree
of synergy between artificial vision and deep learning. Originally, convolutional neural networks (CNNs) were inspired
by the neuroanatomy and layered hierarchy from the retina
1070-9932/20©2020IEEE



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IEEE Robotics & Automation Magazine - June 2020

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