Iontronic tactile sensors are promising to measure spatial-temporal contact information with high performance. However, no suitable measuring method has been presented, due to issues with crosstalk and non-negligible equivalent resistance. Hence, this study presents an impedance-separating method, which does not require complex analog components. A general Quadri-Terminal Impedance Network (QTIN) model is introduced to reduce crosstalk, which has specific compatibility with the impedance-separating method. The precise ranges are measured, showing non-rectangle shapes suitable for the response of iontronic tactile sensors. A simple denoising method is provided to reduce initial array noise obviously. This work could benefit various scenarios, such as human-robot interaction and physiological information monitoring.

Humanoid lower limbs with tactile cognition are crucial for future bipedal robots developing advanced bionic intelligence, such as owning autonomous reflexes and performing human-like actions. Most existing robotic lower limbs focus on providing physical support and mobility, with little work on more bionic DoFs or tactile sensing abilities that are more than significant for a fully humanoid system. This paper develops a robotic lower limb with whole-foot tactile sensing capability. An eight-DoF mechanism with humanoid joints is designed comprehensively, and a tactile sensor with electric double-layer capacitors principle wraps the special-shaped foot surface. An STM32-based circuit integrating perception and control with a real-time inverse kinematics algorithm is experimentally demonstrated. This work provides novel insight and methodology for humanoid robots and tactile-based bionic intelligence.

Human touching the robot to convey intentions or emotions is an essential communication pathway during physical Human-Robot Interaction (pHRI). Therefore, advanced service robots require superior tactile intelligence to guarantee naturalness and safety when making physical contact with human subjects. Tactile intelligence is the capability to percept and recognize tactile information from touch behaviors, in which understanding the physical meaning of touching actions is crucial. For this purpose, this report introduces a recently collected and organized dataset "TacAct" that encloses real-time tactile information when human subjects touched the test device mimicking a robot forearm. The dataset contains 12 types of 24,000 touch actions from 50 subjects. The dataset details are described, the data are preliminarily analyzed, and the validity of the dataset is tested through a convolutional neural network LeNet-5 which classifying different types of touch actions. We believe that the TacAct dataset would be beneficial for the community to understand the touch intention under various circumstances and to develop learning-based intelligent algorithms for different applications.