  Hardness is among the most important attributes of an object that humans
learn about through touch. However, approaches for robots to estimate hardness
are limited, due to the lack of information provided by current tactile
sensors. In this work, we address these limitations by introducing a novel
method for hardness estimation, based on the GelSight tactile sensor, and the
method does not require accurate control of contact conditions or the shape of
objects. A GelSight has a soft contact interface, and provides high resolution
tactile images of contact geometry, as well as contact force and slip
conditions. In this paper, we try to use the sensor to measure hardness of
objects with multiple shapes, under a loosely controlled contact condition. The
contact is made manually or by a robot hand, while the force and trajectory are
unknown and uneven. We analyze the data using a deep constitutional (and
recurrent) neural network. Experiments show that the neural net model can
estimate the hardness of objects with different shapes and hardness ranging
from 8 to 87 in Shore 00 scale.
