1 research outputs found
Ground truth force distribution for learning-based tactile sensing: a finite element approach
Skin-like tactile sensors provide robots with rich feedback related to the
force distribution applied to their soft surface. The complexity of
interpreting raw tactile information has driven the use of machine learning
algorithms to convert the sensory feedback to the quantities of interest.
However, the lack of ground truth sources for the entire contact force
distribution has mainly limited these techniques to the sole estimation of the
total contact force and the contact center on the sensor's surface. The method
presented in this article uses a finite element model to obtain ground truth
data for the three-dimensional force distribution. The model is obtained with
state-of-the-art material characterization methods and is evaluated in an
indentation setup, where it shows high agreement with the measurements
retrieved from a commercial force-torque sensor. The proposed technique is
applied to a vision-based tactile sensor, which aims to reconstruct the contact
force distribution purely from images. Thousands of images are matched to
ground truth data and are used to train a neural network architecture, which is
suitable for real-time predictions.Comment: Accompanying video: https://youtu.be/9A-cONrsiO