  Haptic sensation is an important modality for interacting with the real
world. This paper proposes a general framework of inferring haptic forces on
the surface of a 3D structure from internal deformations using a small number
of physical sensors instead of employing dense sensor arrays. Using machine
learning techniques, we optimize the sensor number and their placement and are
able to obtain high-precision force inference for a robotic limb using as few
as 9 sensors. For the optimal and sparse placement of the measurement units
(strain gauges), we employ data-driven methods based on data obtained by finite
element simulation. We compare data-driven approaches with model-based methods
relying on geometric distance and information criteria such as Entropy and
Mutual Information. We validate our approach on a modified limb of the Poppy
robot and obtain 8 mm localization precision.

Robotics