Advances in Human-Robot Handshaking

The use of social, anthropomorphic robots to support humans in various industries has been on the rise. During Human-Robot Interaction (HRI), physically interactive non-verbal behaviour is key for more natural interactions. Handshaking is one such natural interaction used commonly in many social contexts. It is one of the first non-verbal interactions which takes place and should, therefore, be part of the repertoire of a social robot. In this paper, we explore the existing state of Human-Robot Handshaking and discuss possible ways forward for such physically interactive behaviours.Comment: Accepted at The 12th International Conference on Social Robotics (ICSR 2020) 12 Pages, 1 Figur

Human-Robot Handshaking: A Review

For some years now, the use of social, anthropomorphic robots in various situations has been on the rise. These are robots developed to interact with humans and are equipped with corresponding extremities. They already support human users in various industries, such as retail, gastronomy, hotels, education and healthcare. During such Human-Robot Interaction (HRI) scenarios, physical touch plays a central role in the various applications of social robots as interactive non-verbal behaviour is a key factor in making the interaction more natural. Shaking hands is a simple, natural interaction used commonly in many social contexts and is seen as a symbol of greeting, farewell and congratulations. In this paper, we take a look at the existing state of Human-Robot Handshaking research, categorise the works based on their focus areas, draw out the major findings of these areas while analysing their pitfalls. We mainly see that some form of synchronisation exists during the different phases of the interaction. In addition to this, we also find that additional factors like gaze, voice facial expressions etc. can affect the perception of a robotic handshake and that internal factors like personality and mood can affect the way in which handshaking behaviours are executed by humans. Based on the findings and insights, we finally discuss possible ways forward for research on such physically interactive behaviours.Comment: Pre-print version. Accepted for publication in the International Journal of Social Robotic

Remote control of a robotic hand using a leap sensor

This paper presents a low-cost gesture-based remote control of a ro-botic hand. The proposed control architecture is based on a commercial leap motion sensor and an Arduino board, which have been chosen due to their low-cost and user-friendly features. A specific Matlab code has been implemented to collect data from the leap motion sensor and to generate proper instructions to control a robotic hand, which has been 3D print at Sheffield Hallam Univer-sity. Experimental tests have been carried out validate the effectiveness of the proposed remote control for performing various grasping tasks

Interfaces haptiques pour l'interaction physique humain-robot : analyse et mise en œuvre de l'approche macro-mini

Cette thèse présente la conception d'une interface haptique capable de rendre l'interaction physique humain-robot naturelle et intuitive. Il s'agit là d'un sujet d'étude très important avec l'avènement de la robotique collaborative et la présence toujours accrue des robots dans la vie de tous les jours. Les travaux présentés se concentrent sur l'approche macro-mini à titre d'interface haptique, plus particulièrement trois aspects importants lors de la conception d'un système macro-mini. Le premier chapitre permet d'apprendre à parler physiquement au robot (lui faire comprendre les intentions de l'humain) dans un contexte de déplacement collaboratif. Plus particulièrement, il consiste à comparer différentes méthodes pour traduire les déplacements (ou les intentions) de l'humain à un robot. Dans ce cas, des coquilles à faible impédance sont attachées sur les membrures d'un manipulateur sériel. L'humain interagit avec le robot en déplaçant ces coquilles. La réponse du robot est alors de se déplacer de façon à ce que ses membrures suivent les déplacements de la coquille qui leur est associée pour ainsi les conserver dans leur configuration neutre. Le déplacement d'une coquille par rapport à sa membrure est considéré comme une vitesse désirée de ladite membrure. Il s'agit donc de résoudre le problème cinématique inverse pour traduire le déplacement de la coquille en déplacement articulaire. Cependant, différentes stratégies peuvent être employées pour résoudre ce problème. Ce projet vise donc à comparer l'efficacité de ces méthodes. Pour y parvenir, une étude générale de ces méthodes est réalisée. Puis, un formalisme mathématique est décrit pour adapter ces méthodes à l'application présente. En effet, en fonction du type de coquille et de la membrure, tous les degrés de liberté ne sont pas nécessairement possibles. Ce formalisme mathématique permet de tenir compte de ces contraintes. Ensuite, des simulations sont réalisées pour observer le comportement des méthodes étudiées et un indice de performance est choisi pour les comparer. Ensuite, une fois que le robot est en mesure de comprendre efficacement les intentions humaines, le problème de conception consiste à déterminer comment détecter ses intentions à l'aide d'une interface et surtout, la taille que cette interface doit prendre pour bien parler. En d'autres mots, le second chapitre présente une analyse de l'impact du débattement d'un mécanisme mini actif sur la bande passante mécanique de mouvements possibles lors de la manipulation de charges lourdes. En effet, l'approche macro-mini utilise généralement un robot mini passif, ce qui fait que l'utilisateur ressent toute l'inertie de la charge. Lorsque la charge devient suffisamment lourde, il est nécessaire pour le mini d'appuyer l'utilisateur en fournissant une force pour conserver l'interaction naturelle. Ceci signifie que le mini doit être actionné, i.e., actif. Il est cependant important que le mini reste rétrocommandable pour le bon fonctionnement de l'approche macro-mini. Des modèles mathématiques du système sont donc présentés. Les contraintes relatives à l'application sont décrites ainsi que leur impact sur la bande passante. À l'aide d'un contrôleur simple, des simulations sont réalisées à l'aide des outils développés pour déterminer le débattement nécessaire du mini actif qui permet la bande passante désirée. Enfin, une interface haptique capable de reproduire une poignée de main naturelle et intuitive avec un robot est présentée. Ce chapitre peut être divisé en deux aspects, i.e., la main et le bras. Ici, la main est le robot mini et le bras, le robot macro. D'abord, un prototype de main robotique est conçu et fabriqué. Inspirée de l'anatomie humaine, cette main robotique possède une paume comprimable capable d'émuler celle de l'humain ainsi que trois doigts sous-actionnés. Un pouce passif, relié au niveau de compression de la paume, complète le tout. Le contrôle de la main se fait via une position avec rétroaction, et ce, pour chacun des deux actionneurs (un pour la paume, l'autre pour les trois doigts). Ensuite, la main robotique est montée sur un manipulateur sériel collaboratif (le Kuka LWR), le bras. Ce dernier est contrôlé en impédance autour d'une trajectoire harmonique dans un plan vertical. En fonction des paramètres de la trajectoire (amplitude, fréquence, coefficients d'amortissement et de raideur), ce prototype permet de conférer une personnalité active au robot. L'expérimentation faite auprès de sujets humains permet de déterminer les valeurs considérées plus naturelles pour les différents paramètres de la trajectoire ainsi que diverses pistes à explorer pour des travaux futurs.This thesis presents the design of a haptic interface capable of rendering a physical human-robot interaction natural and intuitive. It is a very important subject to study with the rise in collaborative robots and the ever-increasing presence of robots in everyday life. The work presented here focuses on the macro-mini architecture as haptic interface, more precisely on three important aspects to consider during the design of a macro-mini system. The first chapter explores how to physically communicate with a robot (make it understand the human's intentions) in a context of collaborative motion. In more details, the goal is to compare different methods to translate human motions (or intentions) to a robot. In this case, low impedance passive articulated shells are mounted on the links of a serial manipulator. The human operator interacts with the robot by displacing the shells. The robot's response is then to move so that its links follow the motion of their associated shell. The better the robot can follow the shells, the closest to their neutral configuration the shells can remain. The shell displacement relative to its link is considered as a desired velocity of the link. The translation of the shell displacement into joint motion then becomes an inverse kinematic problem. Different strategies can be used to solve this problem. This project then aims at comparing the efficiency of those strategies. To this end, a general study of the different strategies is performed. Then, a mathematical formalism is described, adapting said strategies to the present context. Indeed, depending on the type of shell and the position of the link in the chain, all degrees of freedom are not necessarily possible. This formalism takes these limitations into account. Then, simulations are conducted to observe the behaviour of the different strategies studied and a performance index is chosen to compare them. Afterwards, once the robot is capable of efficiently understanding the human intentions, the next step is to determine how to detect the intentions with the help of an interface and, most of all, what size should this interface have so as to communicate well. In other words, the second chapter presents an analysis of the impact that the range of motion of an active mini mechanism has on the mechanical bandwidth for the possible motions during the handling of large payloads. Indeed, the macro-mini architecture generally uses a passive mini robot, which means that the human operator feels the whole inertia of the payload. When the payload becomes sufficiently heavy, it becomes necessary for the mini robot to help the operator by working as well so as to keep the interaction natural. This means that the mini robot should then be actuated, i.e., active. It is however important that the mini robot remains backdrivable for the macro-mini architecture to work properly. Mathematical models are then presented. The limitations related to the application are described, as well as their effect on the bandwidth. With the help of a simple controller, simulations are performed with the tools developed to determine the range of motion necessary for the active mini robot which would allow the desired bandwidth. Finally, a haptic interface capable of emulating a natural and intuitive handshake with a robot is presented. This chapter can be divided into two aspects, i.e., the hand and the arm. Here, the hand is the mini robot and the arm, the macro robot. First, a robotic hand prototype is designed and constructed. Inspired by the human anatomy, this robotic hand has a compliant palm able to emulate a human palm as well as three underactuated fingers. A passive thumb, tied to the palm compression level, completes the hand. The hand control is done with position control with feedback for both actuators (one for the palm, the other for the three fingers). Then, the robotic hand is mounted on a collaborative serial manipulator (the Kuka LWR), the arm. The arm is controlled in impedance around a harmonic trajectory in a vertical plane. Depending on the parameters for the trajectory (amplitude, frequency, stiffness and damping coefficients), this prototype provides an active personality to the robot. Experimentation is conducted with human subjects to determine the values considered more natural for the different trajectory parameters as well as several improvements for the prototype in future works

Physical Analysis of Handshaking Between Humans: Mutual Synchronisation and Social Context

International audienceOne very popular form of interpersonal interaction used in various situations is the handshake (HS), which is an act that is both physical and social. This article aims to demonstrate that the paradigm of synchrony that refers to the psychology of individuals' temporal movement coordination could also be considered in handshaking. For this purpose, the physical features of the human HS are investigated in two different social situations: greeting and consolation. The duration and frequency of the HS and the force of the grip have been measured and compared using a prototype of a wearable system equipped with several sensors. The results show that an HS can be decomposed into four phases, and after a short physical contact, a synchrony emerges between the two persons who are shaking hands. A statistical analysis conducted on 31 persons showed that, in the two different contexts, there is a significant difference in the duration of HS, but the frequency of motion and time needed to synchronize were not impacted by the context of an interaction

Human-Robot Collaborations in Industrial Automation

Technology is changing the manufacturing world. For example, sensors are being used to track inventories from the manufacturing floor up to a retail shelf or a customer’s door. These types of interconnected systems have been called the fourth industrial revolution, also known as Industry 4.0, and are projected to lower manufacturing costs. As industry moves toward these integrated technologies and lower costs, engineers will need to connect these systems via the Internet of Things (IoT). These engineers will also need to design how these connected systems interact with humans. The focus of this Special Issue is the smart sensors used in these human–robot collaborations

Cybersecurity and the Digital Health: An Investigation on the State of the Art and the Position of the Actors

Cybercrime is increasingly exposing the health domain to growing risk. The push towards a strong connection of citizens to health services, through digitalization, has undisputed advantages. Digital health allows remote care, the use of medical devices with a high mechatronic and IT content with strong automation, and a large interconnection of hospital networks with an increasingly effective exchange of data. However, all this requires a great cybersecurity commitment—a commitment that must start with scholars in research and then reach the stakeholders. New devices and technological solutions are increasingly breaking into healthcare, and are able to change the processes of interaction in the health domain. This requires cybersecurity to become a vital part of patient safety through changes in human behaviour, technology, and processes, as part of a complete solution. All professionals involved in cybersecurity in the health domain were invited to contribute with their experiences. This book contains contributions from various experts and different fields. Aspects of cybersecurity in healthcare relating to technological advance and emerging risks were addressed. The new boundaries of this field and the impact of COVID-19 on some sectors, such as mhealth, have also been addressed. We dedicate the book to all those with different roles involved in cybersecurity in the health domain

Addressing training data sparsity and interpretability challenges in AI based cellular networks

To meet the diverse and stringent communication requirements for emerging networks use cases, zero-touch arti cial intelligence (AI) based deep automation in cellular networks is envisioned. However, the full potential of AI in cellular networks remains hindered by two key challenges: (i) training data is not as freely available in cellular networks as in other fields where AI has made a profound impact and (ii) current AI models tend to have black box behavior making operators reluctant to entrust the operation of multibillion mission critical networks to a black box AI engine, which allow little insights and discovery of relationships between the configuration and optimization parameters and key performance indicators. This dissertation systematically addresses and proposes solutions to these two key problems faced by emerging networks. A framework towards addressing the training data sparsity challenge in cellular networks is developed, that can assist network operators and researchers in choosing the optimal data enrichment technique for different network scenarios, based on the available information. The framework encompasses classical interpolation techniques, like inverse distance weighted and kriging to more advanced ML-based methods, like transfer learning and generative adversarial networks, several new techniques, such as matrix completion theory and leveraging different types of network geometries, and simulators and testbeds, among others. The proposed framework will lead to more accurate ML models, that rely on sufficient amount of representative training data. Moreover, solutions are proposed to address the data sparsity challenge specifically in Minimization of drive test (MDT) based automation approaches. MDT allows coverage to be estimated at the base station by exploiting measurement reports gathered by the user equipment without the need for drive tests. Thus, MDT is a key enabling feature for data and artificial intelligence driven autonomous operation and optimization in current and emerging cellular networks. However, to date, the utility of MDT feature remains thwarted by issues such as sparsity of user reports and user positioning inaccuracy. For the first time, this dissertation reveals the existence of an optimal bin width for coverage estimation in the presence of inaccurate user positioning, scarcity of user reports and quantization error. The presented framework can enable network operators to configure the bin size for given positioning accuracy and user density that results in the most accurate MDT based coverage estimation. The lack of interpretability in AI-enabled networks is addressed by proposing a first of its kind novel neural network architecture leveraging analytical modeling, domain knowledge, big data and machine learning to turn black box machine learning models into more interpretable models. The proposed approach combines analytical modeling and domain knowledge to custom design machine learning models with the aim of moving towards interpretable machine learning models, that not only require a lesser training time, but can also deal with issues such as sparsity of training data and determination of model hyperparameters. The approach is tested using both simulated data and real data and results show that the proposed approach outperforms existing mathematical models, while also remaining interpretable when compared with black-box ML models. Thus, the proposed approach can be used to derive better mathematical models of complex systems. The findings from this dissertation can help solve the challenges in emerging AI-based cellular networks and thus aid in their design, operation and optimization