This paper deals with the problem of estimating the pose of tactile elements (i.e. taxels) composing a robotic skin covering the whole body of a robot. This problem arises when a robot skin technology has to be integrated into an already existing robotic platform. To date, the integration process is done by hand and it is not possible to predict where the sensor will be placed on the robot body. This paper presents a novel approach based on a RGB-D camera and exploiting the motion capabilities of the robot for activating the skin sensors. The method uses the measurements of the camera to reconstruct the unknown robot body outer shape and to compute how the area can be touched by the robot. The taxels responses and the related contact centroids are used for estimating the position of the sensors. Our method is based on few assumptions and is a step towards a calibration procedure that can be executed autonomously by a robot. Experiments performed on the Baxter robotic platform demonstrate the effectiveness of the presented approach obtaining an average position error less than 2mm.

This paper deals with the problem of using the tactile feedback generated by a robotic skin for discriminating a human hand touch from a generic contact. Humans understand collaboration intentions through different sensing modalities such as vision, hearing and touch. Among them, a physical interaction is mainly used for demonstrating or correcting a kind of motion and is usually started by touching with the hands the other human body. Until recently, it was difficult to perform the same in human-robot cooperation due to the lack of large-scale tactile systems functionally similar to a human skin. Our approach consists in transforming measurements of sensors distributed on the robot body into a convenient 2D representation of the contact shape, i.e., a contact image, then applying image classification techniques in order to discriminate a human touch from unexpected collisions. Experiments have been performed on a robotic skin composed of 768 pressure sensors integrated on a Baxter robot forearm. More than 1800 contact images have been generated from 43 different persons for training and testing two machine learning algorithms: Bag of Visual Words and Convolutional Neural Networks. The experimental results show that both approaches are valid, obtaining a classification accuracy higher than 96%.

This paper presents a novel approach for recognizing a human hand touch by processing pressure measurements generated by a robotic skin. Physical cooperation among humans is mainly based on the sense of touch and usually starts with hand contacts. If a robot can distinguish a human touch from a generic contact, the human-robot cooperation can be more natural and effective. The proposed approach consists in transforming the sensor pressure measurements distributed on the robot surface into a convenient 2D representation of the contact shape, i.e., a contact image. The image-based representation of contacts allows facing the problem of human touch classification by applying machine learning methods already developed for image classification. The experiments have been performed using a robotic skin, composed of 768 tactile elements, placed on a Baxter robot forearm. The contact classification has been performed using a Convolutional Neural Network obtaining an accuracy higher than 97% experimentally validating the proposed approach.

In this paper a novel multi-modal tactile sensor is presented, featuring a matrix of capacitive pressure sensors, a microphone for acoustic measurements and proximity and ambient light sensor. The sensor is fully embedded and can be easily integrated at mechanical and electrical levels with industrial grippers. Tactile sensing design has been put on the same level of additional requirements, usually overlooked in tactile sensor research, such as the mechanical interface, cable harness and robustness against continuous and repetitive operations, just to name but a few. The performances of the different sensing modalities have been assessed in a test rig for tactile sensors. Experiments have been performed in order to show the capabilities of the sensor for implementing tactile based industrial gripper control and tactile based fabric classification.

Within the past decade, extensive research has been done on large-scale tactile sensing, as a result of which, a large variety of robot skins have been developed. These robot skins are different in various aspects: the sensing modality, interconnectivity of the sensors, modularity, the communication network, etc. This variety limits portability of software among these robot skins. In this article, a middleware is proposed that is capable of interacting in principle with any robot skin, through the use of simple drivers. Primarily, the middleware acquires data in real-time and provides its applications with those data in an abstract structure. As a result, the portability of algorithms implemented for large-scale tactile data processing is greatly increased among various available and future robot skins.

In this paper, the description of a sensorized glove that has been developed to perform experiments in robot-based manipulation of clothes and objects is reported. The glove embeds a capacitive tactile sensing technology that has been designed in the past few years. The glove is used to provide an estimate of the expected tactile feedback related to involved forces and contact areas during common manipulation tasks. This information will be used in order to design a robot gripper for cloth manipulation.

This article introduces an extension of the original Parallel Force-Position control framework based on the use of tactile maps. Whole body skin systems for humanoid robots are considered a fundamental feature to improve contact interaction tasks by means of large scale tactile feedback. Stemming from previous work [1], the article describes how it is possible to extend the Parallel Force-Position control paradigm with the use of tactile maps, i.e., computational representation structures encoding the position of tactile elements located on the robot body surface. Furthermore, tactile maps are used to encode specific local features of contact trajectories, such as the desired exerted force at the contact point and the desired velocity. Results in simulation validate the approach.

This paper deals with the problem of estimating the position of tactile elements (i.e. taxels) that are mounted on a robot body part. This problem arises with the adoption of tactile systems with a large number of sensors, and it is particularly critical in those cases in which the system is made of flexible material that is deployed on a curved surface. In this scenario the location of each taxel is partially unknown and difficult to determine manually. Placing the device is in fact an inaccurate procedure that is affected by displacements in both position and orientation. Our approach is based on the idea that it is possible to automatically infer the position of the taxels by measuring the interaction forces exchanged between the sensorized part and the environment. The location of the contact is estimated through force/torque (F/T) measures gathered by a sensor mounted on the kinematic chain of the robot. Our method requires few hypotheses and can be effectively implemented on a real platform, as demonstrated by the experiments with the iCub humanoid robot.

This paper deals with the problem of calibrating a large number of tactile elements (i.e., taxels) organized in a skin sensor system after fixing them to a robot body part. This problem has not received much attention in literature because of the lack of large-scale skin sensor systems. The proposed approach is based on a controlled compliance motion with respect to external objects whose pose is known, which allows a robot to determine the location of its own taxels. The major contribution of this work is the formulation of the skin calibration problem as a maximum-likelihood mapping problem in a 6D space, where both the position and the orientation of each taxel are recovered. An effective calibration process is envisaged that, given a compliance control law that assures prolonged contact maintenance between a given body part and an external object, returns a maximum-likelihood estimate of detected taxel poses. Simulations validate the approach.

In this paper a framework for representing tactile information in robots is discussed. Control models exploiting tactile sensing are fundamental in social Human-Robot interaction tasks. Difficulties arising in rendering the sense of touch in robots are at different levels: both representation and computational issues must be considered. A layered system is proposed, which is inspired from tactile sensing in humans for building artificial somatosensory maps in robots. Experiments in simulation are used to validate the approach.

In this paper a framework for representing tactile information in robots is discussed. Control models exploiting tactile sensing are fundamental in social Human-Robot interaction tasks. Difficulties arising in rendering the sense of touch in robots are at different levels: both representation and computational issues must be considered. A layered system is proposed, which is inspired from tactile sensing in humans for building artificial somatosensory maps in robots. Experiments in simulation are used to validate the approach.

This paper describes a framework for representing physical interaction tasks using tactile feedback. Although contact feedback has been widely exploited to control interaction with objects, the direct use of tactile information in designing and representing physical interaction rules has not received comparable attention in the literature. The missing link between algorithms implementing models of interaction and frameworks providing tactile information is one of the possible reasons. The major contribution of the paper is a working method to build a map of tactile sensors attached to a robot body and a control law using such a map to tune physical interaction with an external object. Experiments are used to validate the approach.