  Soft grippers are gaining significant attention in the manipulation of
elastic objects, where it is required to handle soft and unstructured objects
which are vulnerable to deformations. A crucial problem is to estimate the
physical parameters of a squeezed object to adjust the manipulation procedure,
which is considered as a significant challenge. To the best of the authors'
knowledge, there is not enough research on physical parameters estimation using
deep learning algorithms on measurements from direct interaction with objects
using robotic grippers. In our work, we proposed a trainable system for the
regression of a stiffness coefficient and provided extensive experiments using
the physics simulator environment. Moreover, we prepared the application that
works in the real-world scenario. Our system can reliably estimate the
stiffness of an object using the Yale OpenHand soft gripper based on readings
from Inertial Measurement Units (IMUs) attached to its fingers. Additionally,
during the experiments, we prepared three datasets of signals gathered while
squeezing objects -- two created in the simulation environment and one composed
of real data.
