Model-Based Compliance Discrimination via Soft Tactile Optical Sensing and Optical Flow Computation: A Biomimetic Approach

Abstract

Soft tactile optical sensors have opened up new possibilities for endowing artificial robotic hands with advanced touch-related properties; however, their use for compliance discrimination has been poorly investigated and mainly relies on data-driven methods. Discrimination of object compliance is crucial for enabling accurate and purposeful object manipulation. Humans retrieve this information primarily using the contact area spread rate (CASR) over their fingertips. CASR can be defined as the integral of tactile flow, which describes the movement of iso-strain surfaces within the fingerpad. This work presents the first attempt to discriminate compliance through soft optical tactile sensing based on a computational model of human tactile perception that relies on CASR and tactile flow concepts. To this aim, we used a soft optical biomimetic sensor that transduces surface deformation via movements of marked pins, similar to the function of intermediate ridges in the human fingertip. We acquired images of markers' movements during the interaction with silicone specimens with different compliance at different indenting forces. Then, we computed the optical flow as a tactile flow approximation and its divergence to estimate the CASR. Our model-based approach can accurately discriminate the compliance levels of the specimens, both when the sensor probed the surface perpendicularly and with different inclinations. Finally, we used the relation between specimen compliance and the experimentally evaluated CASR to infer the compliance of a new specimen relying on the estimated CASR.