ML-based tactile sensor calibration: A universal approach
This work addresses sensor calibration for robotics, but it is incremental as it compares existing sensors without introducing new methods.
The study tackled the problem of calibrating tactile sensors by evaluating the iCub fingertip and BioTac sensors under controlled conditions to estimate force and recognize curvature, finding that each sensor excels in different settings.
We study the responses of two tactile sensors, the fingertip sensor from the iCub and the BioTac under different external stimuli. The question of interest is to which degree both sensors i) allow the estimation of force exerted on the sensor and ii) enable the recognition of differing degrees of curvature. Making use of a force controlled linear motor affecting the tactile sensors we acquire several high-quality data sets allowing the study of both sensors under exactly the same conditions. We also examined the structure of the representation of tactile stimuli in the recorded tactile sensor data using t-SNE embeddings. The experiments show that both the iCub and the BioTac excel in different settings.