RoboSense At Edge: Detecting Slip, Crumple and Shape of the Object in Robotic Hand for Teleoprations
This addresses a challenging problem in robotics manipulation for teleoperation applications, but appears incremental as it applies existing ML techniques to a known bottleneck.
The paper tackles slip, crumple, and shape detection for robotic hands in teleoperations like remote surgery, proposing a machine learning model that uses force/torque and actuator angular positions to reduce latency.
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