  Telemanipulation of deformable objects requires high precision and dexterity
from the users, which can be increased by kinesthetic and tactile feedback.
However, the object shape can change dynamically, causing ambiguous perception
of its alignment and hence errors in the robot positioning. Therefore, the tilt
angle and position classification problem has to be solved to present a clear
tactile pattern to the user. This work presents a telemanipulation system for
plastic pipettes consisting of a multi-contact haptic device LinkGlide to
deliver haptic feedback at the users' palm and two tactile sensors array
embedded in the 2-finger Robotiq gripper. We propose a novel approach based on
Convolutional Neural Networks (CNN) to detect the tilt and position while
grasping deformable objects. The CNN generates a mask based on recognized tilt
and position data to render further multi-contact tactile stimuli provided to
the user during the telemanipulation. The study has shown that using the CNN
algorithm and the preset mask, tilt, and position recognition by users is
increased from 9.67% using the direct data to 82.5%.
