In humans, touch-mediated compliance perception integrates sensory feedback with adaptive motor control strategies that regulate internal muscle co-contraction. This mechanism enables the extraction of meaningful information from contact with objects, allowing for precise compliance discrimination. Inspired by this capability, in [1] we developed a biomimetic approach that combines a soft optical tactile sensor, the TacTip −which mimics the main structure of the human fingertip−, with a computational model of human touch (tactile flow) and a single degree of freedom (dof) Variable Stiffness Actuator (VSA), to infer the compliance of the explored specimen. By mapping human muscular co-contraction patterns to the control of the VSA −which emulates the agonist-antagonist behaviour of human muscles–, we demonstrated that our model-based estimation approach−achieved high-accuracy results. In this work, we demonstrate the effectiveness of our method using multi-dofs robotic platforms. The goal is to provide a contribution towards the deployment of robots with advanced perceptual and motor capabilities, working alongside and with humans. We considered a 7-dofs robotic manipulator, the Franka Emika Panda, and mapped human co-contraction profiles through Cartesian Impedance regulation. We achieved a maximum compliance estimation error of 6%, with no statistically significant differences compared to the results obtained with the single-dof VSA, confirming the robustness and generalizability of our technique to more complex robotic systems.

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.