  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 contact object and
their changes with a soft gel pad. The method assumes the object is mostly
rigid and treats the motion of object's imprint on sensor surface as 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 use to detect when small areas of the contact patch start to
slip (incipient slip). The method can detect both translational and rotational
incipient slip without any prior knowledge of the object at 24 Hz. We test the
method on 10 objects 240 times and achieve 86.25% detection accuracy. We
further show how the slip feedback can be used to monitor the gripping force to
avoid slip with a closed-loop bottle-cap screwing and unscrewing experiment
with incipient slip detection feedback. The method was demonstrated to be
useful for the robot to apply proper gripping force and stop screwing at the
right point before breaking objects. The method can be applied to many
manipulation tasks in both structured and unstructured environments.
