Tactile sensing is crucial for the safety, accuracy and robustness of the human-robot interactions in the fields of wearable equipment, service robots and healthcare robots. Although many efforts have been made, it still requires much work to develop functional and reliable flexible tactile sensors with superior sensitivity, wide measurement range, high spatial resolution and low cost based on simple structures and easy fabrication. Here, this paper introduces a flexible supercapacitive tactile sensor with outstanding and balanced performance. The tactile sensor contains two layers of flexible electrodes and a layer of ionic-gel coated microfiber matrix to form a supercapacitive sensing structure. The flexible electrodes and the ionic microfiber matrix are processed with scalable techniques such as screen printing and gel-coating, which guarantee the ultra-flexibility and low fabrication cost. The experimental data suggests strong linearity in the pressure-capacitance relationship and high sensitivity (135.9 nF·kPa-1·cm2 or 27.11 kPa-1). Wide pressure measurement range (from 0 kPa to 1200 kPa) is also achieved by balancing structure parameters. Dynamic responses of the tactile sensors could accurately reflect the applied pressure cycles from human-finger tapping and machine pressing. The tactile sensor can map the pressure distribution with a high spatial resolution (>2 points·mm-2) when connected with the specially designed electric circuitry. The spatial resolution from sub-mm to large area makes it promising for various sensing applications in human-robot interactions, from finger touch to body contact. The developed tactile sensor in this study owns superior applicability and universality which makes it a trustworthy candidate to benefit various applications in robotics, flexible electronics and bioengineered equipment.

Tactile information is essential for safe physical interactions between humans and robots. However, for all kinds of tactile sensing principles, it is still challenging to monitor contact status during physical human-robot interactions (pHRIs) on 3D irregular surfaces due to complex tactile sensor design and signal denoising. This study developed a robotic tactile sensing system by introducing the iontronic electric double-layer capacitor (EDLC) supercapacitive principle, covering a large area (191 mm × 191 mm), and ensuring a dense spatial resolution by using 1024 sensing units. A special grounding strategy and a Spatio-Temporal (S-T) digital filter significantly reduced the crosstalk interference resulting from low ionic mobility and lateral migration. Additionally, based on surface flattening technology, a 3D-curved tactile sensor is presented for tactile sensing of a humanoid lower limb’s foot. Finally, response tests based on human actions were performed by the two kinds of robots. Results showed that the system could rapidly capture the entire dynamic process of pHRI actions and distinguish actions in real-time. This study enables the possibility of tactile sensing with whole-body coverage, rapid responses, and tactile-based robotic intelligence.

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 or continuous analog-to-digital sampling process. A general Quadri -Terminal Impedance Network (QTIN) model is introduced to reduce crosstalk. Based on a crosstalk visualizing platform, the features of concurrent iontronic measuring methods are analyzed, indicating specific merits between the QTIN model and the impedance-separating method. Then, a compensating method was presented to reduce the measuring error caused by temperature and scanning lag. The precise ranges are measured using standard components and compared with the theoretical error, which shows non-rectangle shapes suitable for the response of a homemade iontronic tactile sensor. A simple denoising method is provided to reduce initial array noise. Then, physical human-robot interaction information was analyzed, showing similarities and differences between capacitance and resistance features. For iontronic tactile sensors, this work realized crosstalk-free impedance-separating measurement, which could benefit various scenarios, such as human-robot interaction and physiological information monitoring.

Tactile sensing is an important perceptual modality that enables robots to understand human contact behaviors. Estimating the fingertip contact angle based on tactile pressure distribution provides a simplified representation of the finger’s contact configuration and supports tactile-based perception in human–robot interaction. However, the relationship between tactile pressure distributions and fingertip contact configuration remains insufficiently understood. In this study, a simplified contact mechanics model was employed to investigate the relationship between tactile pressure characteristics and fingertip contact conditions. Theoretical analysis indicates that both the contact area and the contour dimensions of the pressure distribution are influenced by the contact angle and contact force, with varying sensitivities in different directions to these factors. Based on this theory, simplified finite element modeling of the fingertip and multi-subject experiments were conducted. The deformation behavior of the contact region under different contact angles and contact forces was analyzed. The experimental results were generally consistent with the theoretical analysis. Furthermore, contour descriptors were extracted from the tactile pressure distribution to establish a relationship model for estimating the fingertip contact angle, and the model’s accuracy was analyzed. The experimental results indicate that the extracted contour features exhibit systematic variations with contact angle, and the proposed method achieves a mean absolute error (MAE) of 2.73° and a root mean square error (RMSE) of 7.25°. These results demonstrate that tactile pressure contours provide an effective and computationally efficient cue for estimating fingertip contact configuration. This approach may help robots understand human behavior and has potential applications in human–robot interaction and robotic grasping.

Understanding human intentions based on tactile sensing is pivotal for enabling seamless and intuitive human-robot interaction (HRI). By interpreting physical contact, robots can infer human goals, adjust their behavior in real time, and enhance collaboration in shared environments. However, robots cannot fully understand human intent solely from tactile information and often need to rely on other modalities, such as vision. The tactile information generated by finger contact is intrinsically linked to the corresponding motion. Hence, this paper presents a method that estimates human finger motion directly from tactile features obtained during fingertip contact. Motion parameters are represented by finger poses, and tactile features are extracted as contact area, contact position, etc. A linear model is first proposed to describe the relationship between the contact angle and the contact area. To account for the effect of contact forces on the linear relationship, a three-layer structure model (TLSM) of the finger is introduced to characterize the relationship between contact force and contact area. Combining the two models, the contact angle can be estimated. Then, a local pressure center (LPC) algorithm is proposed to determine the contact position of the fingertip. Finally, inverse kinematics is applied to estimate joint angles and reconstruct finger poses. This work developed a method for estimating finger poses via tactile features, which could enhance the perceptual capabilities of robotic systems and be suitable for human-robot interaction.