A Fingertip-Shaped Tactile Sensor with Machine-Learning-Based Sensor-To-Information Processing

Abstract

We present a fingertip-shaped tactile sensor system that can measure static forces and slip vibrations using the same sensor. A fully integrated stress sensor ASIC leads to a simple design and assembly of the tactile fingertip. Instead of calibrating the fingertip to quantitatively measure forces, we use machine learning to extract abstract information out of the raw sensor data. Avoiding complex signal processing, this sensor-to-information processing scheme is fast and can have a small footprint. The results show that the system can classify the direction of applied forces with 99.8% accuracy. The combination of the stress sensor array and the machine learning approach allows to detect slip and tangential force direction simultaneously. The combined classification achieves 99.6% accuracy.