  Tactile sensing has been used for a variety of robotic exploration and
manipulation tasks but a common constraint is a requirement for a large amount
of training data. This paper addresses the issue of data-efficiency by
proposing a novel method for online learning based on a Gaussian Process Latent
Variable Model (GP-LVM), whereby the robot learns from tactile data whilst
performing a contour following task thus enabling generalisation to a wide
variety of tactile stimuli. The results show that contour following is
successful with comparatively little data and is robust to novel stimuli. This
work highlights that even with a simple learning architecture there are
significant advantages to be gained in efficient and robust task performance by
using latent variable models and online learning for tactile sensing tasks.
This paves the way for a new generation of robust, fast, and data-efficient
tactile systems.
