We develop a theoretical super-resolution model for our soft magnetic skin, by which a 15-fold improvement of localization accuracy (from 6mm to 0.4mm) was achieved. Different from the existing super-resolution methods that rely on overlapping signals of neighboring taxels, our model only relies on the local information from a single taxel and thereby can detect multipoint contact applied on neighboring taxels and work properly even when some of the neighboring taxels near the contact position are damaged (or unavailable). 

This video shows the online estimation of force location and magnitude for objects of different shapes and sizes using the proposed super-resolution model.

We design a soft tactile sensor that can measure both normal and shear forces in a decoupled way, and the sensor can locate the contact position at an accuracy finer than its physical resolution.

By mounting our sensor at the fingertip of a robotic gripper, we show that robots can accomplish challenging tasks such as stably grasping fragile objects under external disturbance and threading a needle via teleoperation. 

We propose an approach that can simultaneously localize and segment the hard inclusions (artificial tumor) in artificial tissue via autonomous robotic palpation with a tactile sensor.

Our method is proven to be robust and efficient in both simulation and experiments, which provides new insight into fast tissue abnormalities detection during RMIS and could be beneficial to relevant surgical tasks like tumor removal.

We report a novel texture recognition method by designing a finger-shaped soft tactile sensor and a bi-directional LSTM (Long Short Term Memory) model with the attention mechanism, by which a respective recognition accuracy of 97% for Braille characters and 99% for 60 types of fabrics have been achieved, revealing the effectiveness of our method in surface texture recognition and the potential benefit to various applications, such as Braille reading for visually impaired people and defect detection in the textile industry. 

The video shows the real-time recognition of the Braille poem Dreams with mounting our tactile sensor on a robot arm and human fingertip, respectively.