  Tactile perception is an essential ability of intelligent robots in
interaction with their surrounding environments. This perception as an
intermediate level acts between sensation and action and has to be defined
properly to generate suitable action in response to sensed data. In this paper,
we propose a feedback approach to address robot grasping task using
force-torque tactile sensing. While visual perception is an essential part for
gross reaching, constant utilization of this sensing modality can negatively
affect the grasping process with overwhelming computation. In such case, human
being utilizes tactile sensing to interact with objects. Inspired by, the
proposed approach is presented and evaluated on a real robot to demonstrate the
effectiveness of the suggested framework. Moreover, we utilize a deep learning
framework called Deep Calibration in order to eliminate the effect of bias in
the collected data from the robot sensors.
