Controlling Robot Sliding Relying on Tactile Sensors and Computational Models of Human Touch

While sliding a finger over a surface, the perceived hand position results from musculoskeletal proprioception, motor commands, and tactile motion estimate. When the touched surface has parallel raised ridges, tactile estimate is biased toward a direction perpendicular to the ridges, predicted by the tactile flow model. This illusory effect leads to systematic errors in the reaching movement that depends on ridge orientation and tactile sensitivity. This suggests the fascinating hypothesis that reaching tasks can be used for functional assessment of tactile deficit, a common symptom in several neurological diseases, and for therapeutic intervention. Previously, we demonstrated in simulations how this phenomenon can be used to guide the user's finger sliding on a ridged plate to a target while the user is instructed to move toward another target. This is achieved by designing a Model Predictive Control strategy to estimate optimal ridge orientation at each time instant. In this study, we aim to replicate this behavior with a robotic manipulator endowed with a soft optical tactile sensor to detect surface ridges relying on deep learning techniques to estimate optical flow as tactile flow approximation. A biomimetic robotic architecture replicating in a controllable fashion such behavior represents a unique testbed for neuroscientific investigation and the design of subject-tailored rehabilitation protocols.