Slip classification for dynamic tactile array sensors

The manipulation of objects held in a robotic hand or gripper is accompanied by events such as making and breaking contact and slippage, between the fingertips and the grasped object and between the grasped object and external surfaces. Humans can distinguish among such events, in part, because they excite the various mechanoreceptors in the hands differently. As part of an effort to provide robots with a similar capability, we propose two features that can be extracted from dynamic tactile array data and used to discriminate between hand/object and object/world slips. Both features rely on examining how slippage affects an array of dynamic tactile sensors compared with the way it affects individual elements of the array. In comparison with approaches that require extensive training with particular combinations of objects and skin, the features work for a wide range of frequencies and grasp conditions. The performance and generalizability of the features are verified with testing on three different kinds of sensors and for a range of object textures, grasp forces and slip conditions. Both features demonstrate greater than 85% accuracy in identifying the location of slip.