  Despite significant advances in touch and force transduction, tactile sensing
is still far from ubiquitous in robotic manipulation. Existing methods for
building touch sensors have proven difficult to integrate into robot fingers
due to multiple challenges, including difficulty in covering multicurved
surfaces, high wire count, or packaging constrains preventing their use in
dexterous hands. In this paper, we present a multicurved robotic finger with
accurate touch localization and normal force detection over complex,
three-dimensional surfaces. The key to our approach is the novel use of
overlapping signals from light emitters and receivers embedded in a transparent
waveguide layer that covers the functional areas of the finger. By measuring
light transport between every emitter and receiver, we show that we can obtain
a very rich signal set that changes in response to deformation of the finger
due to touch. We then show that purely data-driven deep learning methods are
able to extract useful information from such data, such as contact location and
applied normal force, without the need for analytical models. The final result
is a fully integrated, sensorized robot finger, with a low wire count and using
easily accessible manufacturing methods, designed for easy integration into
dexterous manipulators.
