Texture sensing is one of the types of information sensed by humans through touch, and is thus of interest to robotics that this type of information can be acquired and processed. In this work we present a texture topography sensor based on a ciliary structure, a biological structure found in many organisms. The device consists of up to 9 elastic cilia with permanent magnetization assembled on top of a highly sensitive tunneling magnetoresistance (TMR) sensor, within a compact footprint of 6×6 mm2. When these cilia brush against some textured surface, their movement and vibrations give rise to a signal that can be correlated to the characteristics of the texture being measured. We also present an electronic signal acquisition board, used in this work. Various configurations of cilia sizes are tested, with the most precise being capable of differentiating different types of sandpaper from 9.2 μm to 213 μm average surface roughness with a 7 μm resolution. As a topography scanner the sensor was able to scan a 20 μm high step in a flat surface.

The growing consumer demand for large volumes of high quality fruit has generated an increasing need for auto-mated fruit quality control during production. Optical methods have been proved successful in a few cases, but with limitations related to the variability of fruit colors and lighting conditions during tests. Tactile sensing provides a valuable alternative, although it comes with the need of a physical interaction that could damage the fruit. To overcome these limitations, we propose the usage of a recently developed soft tactile sensor for non-invasive fruit quality control. The ability of the sensor to detect very small forces and to finely analyze surfaces allows the collection of relevant information about the fruit by performing a very delicate physical interaction, that does not cause any damage. We report experiments in which such information is used to determine whether apples and strawberries are ripe or senescent. We test different configurations of the sensor and different classification algorithms, achieving very good accuracy for both apples (96%) and strawberries (83%).

In this paper, we study the influence of a handshake in the human emotional bond to a robot. In particular, we evaluate the human willingness to help a robot whether the robot first introduces itself to the human with or without a handshake. In the tested paradigm the robot and the human have to perform a joint task, but at a certain stage, the robot needs help to navigate through an obstacle. Without requesting explicit help from the human, the robot performs some attempts to navigate through the obstacle, suggesting to the human that it requires help. In a study with 45 participants, we measure the human's perceptions of the social robot Vizzy, comparing the handshake vs non-handshake conditions. In addition, we evaluate the influence of a handshake in the pro-social behaviour of helping it and the willingness to help it in the future. The results show that a handshake increases the perception of Warmth, Animacy, Likeability, and the tendency to help the robot more, by removing the obstacle.

In this paper we present a low-cost and easy to fabricate 3-axis tactile sensor based on magnetic technology. The sensor consists in a small magnet immersed in a silicone body with an Hall-effect sensor placed below to detect changes in the magnetic field caused by displacements of the magnet, generated by an external force applied to the silicone body. The use of a 3-axis Hall-effect sensor allows to detect the three components of the force vector, and the proposed design assures high sensitivity, low hysteresis and good repeatability of the measurement: notably, the minimum sensed force is about 0.007N. All components are cheap and easy to retrieve and to assemble; the fabrication process is described in detail and it can be easily replicated by other researchers. Sensors with different geometries have been fabricated, calibrated and successfully integrated in the hand of the human-friendly robot Vizzy. In addition to the sensor characterization and validation, real world experiments of object manipulation are reported, showing proper detection of both normal and shear forces.

The application of learning-based vision techniques to real scenarios usually requires a tunning procedure, which involves the acquisition and labeling of new data and in situ experiments in order to adapt the learning algorithm to each scenario. We address an automatic update procedure of the L2boost algorithm that is able to adapt the initial models learned off-line. Our method is named UAL2Boost and present three new contributions: (i) an on-line and continuous procedure that updates recursively the current classifier, reducing the storage constraints, (ii) a probabilistic unsupervised update that eliminates the necessity of labeled data in order to adapt the classifier and (iii) a multi-class adaptation method. We show the applicability of the on-line unsupervised adaptation to human action recognition and demonstrate that the system is able to automatically update the parameters of the L2boost with linear temporal models, thus improving the output of the models learned off-line on new video sequences, in a recursive and continuous way. The automatic adaptation of UAL2Boost follows the idea of adapting the classifier incrementally: from simple to complex.