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

RoboSense At Edge: Detecting Slip, Crumple and Shape of the Object in Robotic Hand for Teleoprations