Incipient Slip Detection with a Biomimetic Skin Morphology

Incipient slip is defined as the slippage of part, but not all, of the contact surface between a sensor and an object. Reliably detecting incipient slip in artificial tactile sensors would benefit autonomous robot handling capabilities by helping prevent object slippage during manipulation. Here, we present a biomimetic skin morphology based on the human fingerprint with application to marker-based tactile sensors such as the TacTip biomimetic optical tactile sensor. We modify the 3D-printed outer membrane of the TacTip to mimic glabrous skin morphology with the inclusion of external ridges (fingerprint) and internal markers (intermediate ridges), allowing localised shear deformation of the sensor's skin prior to the onset of gross slip. To validate the performance of this skin morphology, we train a random forest classifier (RFC) to identify incipient slip based on the extracted marker displacements from the sensor when it is compressed against an acrylic plate and moved laterally. The RFC model achieves 97.46% accuracy on incipient slip prediction, and is then validated on an unseen pouring task, in which gravity-induced incipient slip is detected on average within
$418\pm 753\ \text{ms}$
of its onset, and before gross slip in all trials. This accurate detection of incipient slip enables corrective actions prior to the onset of gross slip, a key capability in robotic manipulation and upper-limb prosthetics.