• compliance discrimination
• robotic palpation
• tactile sensing
• human-like impedance control

Replicating the sense of touch in robots remains one of the most significant challenges in robotics. Humans often take for granted their ability to feel and perceive the physical world through touch, even though it is a crucial aspect of how they interact with their surroundings. In recent years, numerous researchers have worked to bridge the gap between humans and robots by equipping robots with advanced tactile systems capable of simulating the human sense of touch with a surprising degree of accuracy. These innovative systems include Soft Optical Tactile Sensors like the BRL TacTip, which have not been extensively used for compliance discrimination.
This study introduces a system for recognizing the compliance levels of unknown objects that mimics the human muscular co-contraction strategy during object exploration. This system exploits the variable stiffness actuator, whose impedance is derived from EMG signals collected during object investigation by a participant, and the TacTip sensor, which replicates the structure of the human fingerpad.
The main objective of this work is to demonstrate how the model-based method, relying on a biomimetic approach, can be enhanced by incorporating human-like impedance control during object exploration, particularly during sensor-object contact.