Integrating Human-Like Impedance Regulation and Model-Based Approaches for Compliance Discrimination via Biomimetic Optical Tactile Sensors

Endowing robots with advanced tactile abilities based on biomimicry involves designing human-like tactile sensors, computational models, and motor control policies to enhance contact information retrieval. Here, we consider compliance discrimination with a soft biomimetic tactile optical sensor (TacTip). In previous work, we proposed a vision-based approach derived from a computational model of human tactile perception to discriminate object compliance with the TacTip, based on contact area spread computation over the indenting force. In this work, we first increased the robustness of our vision-based method with a more precise estimation of the initial contact area condition, which enables correct compliance estimation also when the probing direction is other than normal to the specimen surface. Then, we integrated within our validated framework the mechanisms of internal muscular regulation (co-contraction) that humans adopt during object compliance probing, to maximize the information uptake. To this aim, we used human co-contraction patterns extracted during object softness probing to control a Variable Stiffness Actuator (that emulates the agonistic-antagonistic behavior of human muscles), which is used to actuate the indenter system endowed with the TacTip for object compliance exploration. We found that our model-based approach for compliance discrimination, fed with more precisely estimated initial conditions, significantly improves with the human-inspired impedance regulation, with respect to the usage of a rigid actuator.