Learning to “see” Touch

Robotics is breaking free from constrained environments to physically interact with a variety of objects and humans. This advanced level of interaction can be supported by multi-sensor systems capable of perceiving the objects around them, changes to the environment, and the actions of human users. Machine touch, in particular, can play a key role within the context of a multi-sensor object recognition system, enabling robustness to illumination changes and occlusions, extraction of object properties, etc. The majority of tactile recognition systems are based on grasping, however, recently, object contact through a continuous tactile sensor, i.e., artificial skin, has emerged as a powerful alternative. In this lecture, we will discuss artificial skin sensing using Electrical Impedance Tomography (EIT). For a set of typical EIT measurements, one or more rings of electrodes are placed around the medium that an object is positioned. A small current is injected into an electrode pair and the voltages between the other pairs of electrodes are recorded. EIT is capable of simultaneously detecting multiple objects, while providing a richer, i.e., image-based, description of their properties. This offers a number of advantages, e.g., low cost, wire-free, good temporal resolution, and, in the context of tactile sensing, a straightforward interpretation of pressure information. Specifically, when pressure is applied to a pressure-sensitive artificial skin, the surface of the skin deforms, resulting to changes in its conductivity distribution, which can be represented as an image through EIT. To empower EIT for tactile sensing, machine learning is used to solve the complex problem of image reconstruction through patch-based decomposition. Determining the location of the tactile stimulus is posed as an image segmentation process, which is addressed using transfer learning. Finally, features extracted from the segmented objects are used for classification with state-of-the-art machine learning methods.