Slip interface classification through tactile signal coherence

The manipulation of objects in a hand or gripper is typically accompanied by events such as slippage, between the fingers and a grasped object or between the object and external surfaces. Humans can identify such events using a combination of superficial and deep mechanoreceptors. In robotic hands, with more limited tactile sensing, such events can be hard to distinguish. This paper presents a signal processing method that can help to distinguish finger/object and object/world events based on multidimensional coherence, which measures whether a group of signals are sampling a single input or a group of incoherent inputs. A simple linear model of the fingertip/object interaction demonstrates how signal coherence can be used for slip classification. The method is evaluated through controlled experiments that produce similar results for two very different tactile sensing suites.