Maintaining Grasps within Slipping Bounds by Monitoring Incipient Slip

In this paper, we propose an approach to detect incipient slip, i.e. predict slip, by using a high-resolution vision-based tactile sensor, GelSlim. The sensor dynamically captures the tactile imprints of the grasped object and their changes with a soft gel pad. The method assumes the object is mostly rigid and expects the motion of object's imprint on the sensor surface to be a 2D rigid-body motion. We use the deviation of the true motion field from that of a 2D planar rigid transformation as a measure of slip. The output is a dense slip field which we monitor in real time to detect when small areas of the contact patch start to slip (incipient slip). The method can detect incipient slip in any direction without any prior knowledge of the object at 24 Hz. We test the method on 10 objects for 240 times and achieve 86.25% detection accuracy with the vast majority of failure cases occurring when grasping highly deformable objects. We further show how the slip feedback can be used to adjust the gripping force to avoid slip with a closed-loop bottle-cap screwing and unscrewing experiment. The method can be used to enable many manipulation tasks in both structured and unstructured environments.