  Slip and crumple detection is essential for performing robust manipulation
tasks with a robotic hand (RH) like remote surgery. It has been one of the
challenging problems in the robotics manipulation community. In this work, we
propose a technique based on machine learning (ML) based techniques to detect
the slip, and crumple as well as the shape of an object that is currently held
in the robotic hand. We proposed ML model will detect the slip, crumple, and
shape using the force/torque exerted and the angular positions of the actuators
present in the RH. The proposed model would be integrated into the loop of a
robotic hand(RH) and haptic glove(HG). This would help us to reduce the latency
in case of teleoperation
