  This paper describes the design of a multi-camera optical tactile sensor that
provides information about the contact force distribution applied to its soft
surface. This information is contained in the motion of spherical particles
spread within the surface, which deforms when subject to force. The small
embedded cameras capture images of the different particle patterns that are
then mapped to the three-dimensional contact force distribution through a
machine learning architecture. The design proposed in this paper exhibits a
larger contact surface and a thinner structure than most of the existing
camera-based tactile sensors, without the use of additional reflecting
components such as mirrors. A modular implementation of the learning
architecture is discussed that facilitates the scalability to larger surfaces
such as robotic skins.
