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Reminding is often identified as a central function of socially assistive robots in the healthcare sector. The robotic reminders are supposed to help people with memory impairments to remember to take their medicine, to drink and eat, or to attend appointments. Such standalone reminding technologies can, however, be too demanding for people with memory injuries. In a co-creation process, we developed an individual reminder robot together with a person with traumatic brain injury and her care personnel. During this process, we learned that while current research describe reminding as a prototypical task for socially assistive robots, there is no clear definition of what constitutes a reminder nor that it is based on complex sequences of interactions that evolve over time and space, across different actions, actors and technologies. Based on our data from the co-creation process and the first deployment, we argue for a shift towards a sequential and socially distributed character of reminding. Understanding socially assistive robots as rehabilitative tools for people with memory impairment, they need to be reconsidered as interconnected elements in institutional care practices instead of isolated events for the remindee. [ABSTRACT FROM AUTHOR]

In a human–robot conversation, it is difficult for the robot to start the conversation just when the addressee is ready to listen to the robot, due to recognition technology issues. This paper proposes and evaluates a method to reduce the sense of that the timing of starting the conversation is bad. In this method, two robots perform a cooperative behavior during a waiting time from the call for attracting addressee's attention to the main utterance the robot want to deliver. To evaluate the effectiveness of this method, we conducted an experiment that compared three conversation initiation approaches: early timing and late timing by one robot, and the proposed approach involving two robots. The results revealed that the proposed method mitigates the bad timing of main utterances as perceived by the participants. [ABSTRACT FROM AUTHOR]

Human-Robot interaction plays a vital role in the service robotics field, especially to support old age-dependent people for socio-economic reasons. In this paper, an indigenous 3D printed 6 DoF robotic arm is proposed to support specially-abled people in their independent-feeding process. The objective of the present paper is to find the combination of optimal positional controllers such as CPID, FC, FPID and FOPID, which can handle the cubic reference input signal and can produce an output signal with minimal overshoot and lesser positional error. The reduced positional error helps the robotic arm to accurately reach the destination with minimal oscillations. This would reduce the wastage of food in the middle of the trajectory as well as at the destination. The technical challenge of the paper is to synchronize machine vision, robot kinematics and trajectory planning with robot control for multiple intermediate points, subjected to the cubic input signal. The feeding robotic arm is equipped with Intel© visual depth camera, which is in synchronization with the microcontroller, servo controller, and servo actuators. Here, six intermediate points were identified in the C-space using FK, among which, fuzzy controller was selected for the first two IPs, the GA optimized FOPID was selected for IP3 to IP5 and FPID was deployed for the last IP. The GA: FOPID produced a negligible overshoot of 0.67%, whereas FC and FPID produced an overshoot of 1.4% and 0.8% respectively. The positional error gap between GA: FOPID, FC and FPID was 0.9, and 0.6 Deg/sec respectively. The selection of combination of optimal controllers helps the manipulator to successfully deliver the food without wasting it. The repeatability of the feeding process is ensured by successfully conducting user testing on 20 users for 25 cycles. [ABSTRACT FROM AUTHOR]

The mindBot robot is a new educational robot toy that can be controlled by brain signals and voice commands. It was evaluated with children with disabilities as well as healthy children as the potential users. The most significant challenge was the size of the used Emotiv Insight electroencephalogram headset when adjusting it on the children's' heads. Despite all the challenges, the mindBot robot is a promising technology that could be fun and educational for disabled children. The 11 participants took 36 minutes to finish all tasks on average. This includes the time they spent setting up the robot for the first time, putting on the headset, learning how to use the robot, and using the main educational features. The System Usability Scale usability score for the robot is 71.13, which is considered to be the score of good. The future stages of improving the mindBot includes adding more mobility capabilities and adding the feature of educational assessment. [ABSTRACT FROM AUTHOR]

Robotics is a cross-disciplinary branch of science and technology, and lays foundation on mechanics, control, computer science, artificial intelligence, and so on. With the developments of both softwares and hardwares, especially in the artificial intelligence technologies, robots have been widely applied in multiple areas in the society, and become more and more interactive in our daily life, such as the service robots in the museums, shopping malls, restaurants, etc. Though the ultimate goal for a service robot to behave like a human is not easy to be achieved, significant processes have been made during the past decades. Considering that it is universal that service robots are triggered to execute tasks specified by human users via commands (Comm-TET), and it is essential to process and understand human users' commands correctly, we comprehensively overview the developments of the task understanding (TU) sub-process of Comm-TET for service robots. In order to organize the related literature in a reasonable manner, we abstracted the pipeline of Comm-TET and the generic framework of TU based on the existing researches. Following the abstracted framework, we present in-depth discussions on each of its building blocks over the past decades, and give some insights on the future research directions. Compared to other reviews on TU, this review emphasizes more on the technical developments and organizes the existing researches as an integrality. [ABSTRACT FROM AUTHOR]

Robot vision provides the most important information to robots so that they can read the context and interact with human partners successfully. Moreover, to allow humans recognize the robot's visual understanding during human-robot interaction (HRI), the best way is for the robot to provide an explanation of its understanding in natural language. In this paper, we propose a new approach by which to interpret robot vision from an egocentric standpoint and generate descriptions to explain egocentric videos particularly for HRI. Because robot vision equals to egocentric video on the robot's side, it contains as much egocentric view information as exocentric view information. Thus, we propose a new dataset, referred to as the global, action, and interaction (GAI) dataset, which consists of egocentric video clips and GAI descriptions in natural language to represent both egocentric and exocentric information. The encoder-decoder based deep learning model is trained based on the GAI dataset and its performance on description generation assessments is evaluated. We also conduct experiments in actual environments to verify whether the GAI dataset and the trained deep learning model can improve a robot vision system [ABSTRACT FROM AUTHOR]

Consistency of tracking filter such as Interactive Multiple Model Probabilistic Data Association Filter (IMMPDAF) is the most important factor in targets tracking. Inaccurate tracking capability will lead to poor tracking performance when dealing with multiple people's interactions and occlusions. In order to validate the consistency, Normalized Estimation Error Squared (NEES) and Normalized Innovation Squared (NIS) were evaluated and tested using Monte Carlo experiments for 50 runs. These simulations has proven that the tracker is conditionally consistent on targets tracking despite the fact that it has difficulties on handling occlusions and maneuvering people. NEES requires ground truth of tracking data and predicted data, whereas NIS requires observation and predicted data for Monte Carlo simulations. In NEES simulations, the result emphasizes that state estimation errors of IMMPDAF tracker are inconsistent with filter-calculated covariances especially when dealing with sudden turns in zig-zag motion where quite a large number of points fall outside 95% probability region. In NIS simulations, IMMPDAF tracker is confirmed to have difficulties to handle multiple targets with a short period of occlusion although a small number of points falls outside of 95% probability region. Filter tracker is considered mismatched when dealing with zig-zag motion; however, it deemed to be optimistic when dealing with occlusions. As a result, the IMMPDAF tracker has limited capability in monitoring sharp turns under occlusion conditions, although it is acceptable when dealing with occlusions only. [ABSTRACT FROM AUTHOR]

We studied politeness in human–robot interaction based on Lakoff's politeness theory. In a series of eight studies, we manipulated three different levels of politeness of non-humanoid robots and evaluated their effects. A table-setting task was developed for two different types of robots (a robotic manipulator and a mobile robot). The studies included two different populations (old and young adults) and were conducted in two conditions (video and live). Results revealed that polite robot behavior positively affected users' perceptions of the interaction with the robots and that participants were able to differentiate between the designed politeness levels. Participants reported higher levels of enjoyment, satisfaction, and trust when they interacted with the politest behavior of the robot. A smaller number of young adults trusted the politest behavior of the robot compared to old adults. Enjoyment and trust of the interaction with the robot were higher when study participants were subjected to the live condition compared to video and participants were more satisfied when they interacted with a mobile robot compared to a manipulator. [ABSTRACT FROM AUTHOR]

This paper presents the results of a three-week in-the-wild deployment of a wizarded service robot in a shared campus workplace. The study introduces robot-centric ethnography, a concept in which a wizarded robot acts as a mediated anthropologist, used in this case, to further our understandings of how service robots impact and integrate into everyday workplace experiences. Our research site included participants familiar with robots, recruited from 90+ students and faculty working in a shared lab space. Our wizarding team visited these participants each workday they were there for three weeks, navigating open office and lab spaces to remind participants to be aware of their mental, physical, and nutritional health needs. Using a semi-structured format, the wizards adapted the standard interaction flow to the situation. This interaction sequence was guided via pre-populated buttons on our health coach interface, with human wizards triggering the timing and adding extra responses as felt natural. Our ethnography-informed approach used the social knowledge of both participants and wizards, blending the robot into the cultural environment in which it was operating. Our data supports the positive impact of fluent service robot experience on participant mood and overall workplace experience. This suggests that effectively designed service robots can benefit workplace environments above and beyond their intended functions. [ABSTRACT FROM AUTHOR]

Background: Although robotic manipulators have great potential in promoting motor independence of people with motor impairments, only few systems are currently commercially available. In addition to technical, economic, and normative barriers, a key challenge for their distribution is the current lack of evidence regarding their usefulness, acceptance, and user-specific requirements. Objective: Against this background, a semiautonomous robot system was developed in the research and development project, robot-assisted services for individual and resource-oriented intensive and palliative care of people with amyotrophic lateral sclerosis (ROBINA), to support people with amyotrophic lateral sclerosis (ALS) in various everyday activities. Methods: The developed early-stage demonstrator was evaluated in a task-based laboratory study of 11 patients with ALS. On the basis of a multimethod design consisting of standardized questionnaires, open-ended questions, and observation protocols, participants were asked about its relevance to everyday life, usability, and design requirements. Results: Most participants considered the system to provide relevant support within the test scenarios and for their everyday life. On the basis of the System Usability Scale, the overall usability of the robot-assisted services for individual and resource-oriented intensive and palliative care of people with ALS system was rated as excellent, with a median of 90 (IQR 75-95) points. Moreover, 3 central areas of requirements for the development of semiautonomous robotic manipulators were identified and discussed: requirements for semiautonomous human-robot collaboration, requirements for user interfaces, and requirements for the adaptation of robotic capabilities regarding everyday life. Conclusions: Robotic manipulators can contribute to increase the autonomy of people with ALS. A key issue for future studies is how the existing ability level and the required robotic capabilities can be balanced to ensure both high user satisfaction and effective and efficient task performance. [ABSTRACT FROM AUTHOR]

Multi-modal behavior for social robots is crucial for the robot's perceived social intelligence, ability to communicate nonverbally, and the extent to which the robot can be trusted. However, most of the research conducted so far has been with only one modality, thus there is still a lack of understanding of the effect of each modality when performed in a multi-modal interaction. This study presents a multi-modal interaction focusing on the following modalities: proxemics for social navigation, gaze mechanisms (for turn-taking floor-holding, turn-yielding and joint attention), kinesics (for symbolic, deictic, and beat gestures), and social dialogue. The multi-modal behaviors were evaluated through an experiment with 105 participants in a seven minute interaction to analyze the effects on perceived social intelligence through both objective and subjective measurements. The results show various insights of the effect of modalities in a multi-modal interaction onto several behavioral outcomes of the users, including taking physical suggestions, distances maintained during the interaction, wave gestures performed in greeting and closing, back-channeling, and how socially the robot is treated, while having no effect on self-disclosure and subjective liking. [ABSTRACT FROM AUTHOR]

Robotics has a special place in AI as robots are connected to the real world and robots increasingly appear in humans everyday environment, from home to industry. Apart from cases were robots are expected to completely replace them, humans will largely benefit from real interactions with such robots. This is not only true for complex interaction scenarios like robots serving as guides, companions or members in a team, but also for more predefined functions like autonomous transport of people or goods. More and more, robots need suitable interfaces to interact with humans in a way that humans feel comfortable and that takes into account the need for a certain transparency about actions taken. The paper describes the requirements and state-of-the-art for a human-centered robotics research and development, including verbal and non-verbal interaction, understanding and learning from each other, as well as ethical questions that have to be dealt with if robots will be included in our everyday environment, influencing human life and societies. [ABSTRACT FROM AUTHOR]

A collaborative mobile robot has been developed for tea plucking in narrow terrain tea gardens. The robot executes stable side-by-side motions and carries most of the load of the tea harvesting machine. The robot is controlled with adjustable autonomy so that the human can provide a supervisory role and adjust the optimal height and forward speed of the cutting tool. During tea plucking, the robot can autonomously detect the tree rows and avoid colliding with the trees. The experimental results show that the proposed collaborative robot can stably co-work with humans and significantly improve working efficiency and comfort. [ABSTRACT FROM AUTHOR]

The use of non-industrial robots, called service robots, is increasing in the welfare fields to meet the demand for robot therapy among individuals with autism. The more simple communication structures and repetitive behaviors of robots, compared to humans, make it easier for children with autism to interpret communication and respond appropriately. Interacting with a robot allows for social distance to be designed and maintained depending on a person's social interaction needs. To simulate natural social interactions, robots need to perform social distance in some way. In the context of interacting with autistic children, understanding their social response levels is crucial for the robot to implement decisions regarding the distance kept during the interaction. In this study, an experiment was conducted to examine the accuracy of a detection program and explore the correlations between the social responsiveness of children and social distance, wherein 15 autistic children interacted with a robot on a one-to-one basis for about 20 min. The results revealed that both programs implemented in the robot-assisted autism therapy were effective in detecting social distance in a natural HRI situation. [ABSTRACT FROM AUTHOR]

• Novel approach to identity verification in Human-Robot interaction. • Identity verification produced from both sound (voice) and image (face) inputs. • Proposed approaches focus on identifying the parts of the signal that contains the high value information. • Through analysis of regions of interest substantial improvements are obtained over the state of the art. • Evaluation of performance is obtained in a complex dataset that portrays real life scenarios. In this paper, we tackle the task of improving biometric verification in the context of Human-Robot Interaction (HRI). A robot that wants to identify a specific person to provide a service can do so by either image verification or, if light conditions are not favourable, through voice verification. In our approach, we will take advantage of the possibility a robot has of recovering further data until it is sure of the identity of the person. The key contribution is that we select from both image and audio signals the parts that are of higher confidence. For images we use a system that looks at the face of each person and selects frames in which the confidence is high while keeping those frames separate in time to avoid using very similar facial appearance. For audio our approach tries to find the parts of the signal that contain a person talking, avoiding those in which noise is present by segmenting the signal. Once the parts of interest are found, each input is described with an independent deep learning architecture that obtains a descriptor for each kind of input (face/voice). We also present in this paper fusion methods that improve performance by combining the features from both face and voice, results to validate this are shown for each independent input and for the fusion methods. [ABSTRACT FROM AUTHOR]

The poor performances of typically developing children younger than 4 in the first-order false-belief task "Maxi and the chocolate" is analyzed from the perspective of conversational pragmatics. An ambiguous question asked by an adult experimenter (perceived as a teacher) can receive different interpretations based on a search for relevance, by which children according to their age attribute different intentions to the questioner, within the limits of their own meta-cognitive knowledge. The adult experimenter tells the child the following story of object-transfer: "Maxi puts his chocolate into the green cupboard before going out to play. In his absence, his mother moves the chocolate from the green cupboard to the blue one." The child must then predict where Maxi will pick up the chocolate when he returns. To the child, the question from an adult (a knowledgeable person) may seem surprising and can be understood as a question of his own knowledge of the world, rather than on Maxi's mental representations. In our study, without any modification of the initial task, we disambiguate the context of the question by (1) replacing the adult experimenter with a humanoid robot presented as "ignorant" and "slow" but trying to learn and (2) placing the child in the role of a "mentor" (the knowledgeable person). Sixty-two typical children of 3 years-old completed the first-order false belief task "Maxi and the chocolate," either with a human or with a robot. Results revealed a significantly higher success rate in the robot condition than in the human condition. Thus, young children seem to fail because of the pragmatic difficulty of the first-order task, which causes a difference of interpretation between the young child and the experimenter. [ABSTRACT FROM AUTHOR]

The evolution of production systems for smart factories foresees a tight relation between human operators and robots. Specifically, when robot task reconfiguration is needed, the operator must be provided with an easy and intuitive way to do it. A useful tool for robot task reconfiguration is Programming by Demonstration (PbD). PbD allows human operators to teach a robot new tasks by showing it a number of examples. The article presents two studies investigating the role of the robot in PbD. A preliminary study compares standard PbD with human–human teaching and suggests that a collaborative robot should actively participate in the teaching process as human practitioners typically do. The main study uses a wizard of oz approach to determine the effects of having a robot actively participating in the teaching process, specifically by controlling the end-effector. The results suggest that active behaviour inspired by humans can lead to a more intuitive PbD. [ABSTRACT FROM AUTHOR]

With the advancement of robotics, the importance of service robots in society is increasing. It is crucial for service robots to understand their environment so that they can offer suitable responses to humans. To realize the use of space, robots primarily use an environment model. This paper is focused on the development of an environment model based on human behaviors. In this model, a new neural network structure called dynamic highway networks is applied to recognize humans' behaviors. In addition, a two-dimensional pose estimator, Laban movement analysis, and the fuzzy integral are employed. With these methods, two new behavior-recognition algorithms are developed, and a method to record the relationship between behavior and environment is proposed. Based on the proposed environmental model, robots can identify abnormal behavior, provide an appropriate response and guide a person toward the desired normal behavior by identifying abnormal behavior. Simulations and experiments justify the proposed method with satisfactory results. [ABSTRACT FROM AUTHOR]

Robots might not act according to human expectations if they cannot anticipate how people make sense of a situation and what behavior they consider appropriate in some given circumstances. In many cases, understanding, expectations and behavior are constrained, if not driven, by culture, and a robot that knows about human culture could improve the quality level of human–robot interaction. Can we share human culture with a robot? Can we provide robots with formal representations of different cultures? In this paper, we discuss the (elusive) notion of culture and propose an approach based on the notion of trait which, we argue, permits us to build formal modules suitable to represent culture (broadly understood) in a robot architecture. We distinguish the types of traits that such modules should contain, namely behavior, knowledge, rule and interpretation traits, and how they could be organized. We identify the interpretation process that maps situations to specific knowledge traits, called scenarios, as a key component of the trait-based culture module. Finally, we describe how culture modules can be integrated in an existing architecture, and discuss three use cases to exemplify the advantages of having a culture module in the robot architecture highlighting surprising potentialities. [ABSTRACT FROM AUTHOR]

Humanoid robots should play an increasing role in hospitality and tourism services. Anthropomorphic – human like – characteristics seem critical component to consumers accepting robotic service (rService). This conceptual manuscript advances rService research by drawing on services marketing, Human Robot Interaction (HRI) and the Uncanny Valley Theory to explore anthropomorphic characteristics' range, role and impact on rService experiences. The paper proposes eleven robot capabilities that influence anthropomorphism and consequently shape HRI, three Uncanny Valley marketing outcomes, theoretical concepts, and a rich future research agenda. Hospitality and tourism literature and examples highlight the service context's importance when researching, adopting, implementing and marketing rServices. [ABSTRACT FROM AUTHOR]

Objective: Modeling of human motor intention plays an essential role in predictively controlling a robotic system in human–robot interaction tasks. In most machine learning techniques, human motor behavior is modeled as a generic stochastic process. However, the integration of a priori knowledge about underlying system structures can provide insights on otherwise unobservable intrinsic states that yield the superior prediction performance and increased generalization capabilities. Methods: We present a novel method for modeling human motor behavior that explicitly includes a neuroscientifically supported model of human motor control, in which the dynamics of the human arm are modeled by a mechanical impedance that tracks a latent desired trajectory. We adopt a Bayesian setting by defining Gaussian process (GP) priors for the impedance elements and the latent desired trajectory. This enables exploitation of a priori human arm impedance knowledge for regression of interaction forces through inference of a latent desired human trajectory. Results: The method is validated using simulated data, with particular focus on effects of GP prior parameterization and intention estimation capabilities. The superior prediction performance is shown with respect to a naive GP prior. An experiment with human participants evaluates generalization capabilities and effects of training data sparsity. Conclusion: We derive the correlations of an impedance-based GP model of human motor behavior that exploits a priori knowledge. Significance: The model effectively predicts interaction forces by inferring a latent desired human trajectory in previously observed as well as unobserved regions of the input space. [ABSTRACT FROM AUTHOR]

Conversational robots have been used to convey information to people in the real world. Android robots, which have a human-like appearance, are expected to be able to convey not only objective information but also subjective information, such as a robot's feelings. Meanwhile, as an approach to realize attractive conversation, multi-party conversation by multiple robots was the focus of this study. By collaborating among several robots, the robots provide information while maintaining the naturalness of conversation. However, the effectiveness of interaction with people has not been surveyed using this method. In this paper, to develop more efficient media to convey information, we propose a scenario-based, semi-passive conversation system using two androids. To verify its effectiveness, we conducted a subjective experiment comparing it to a system that does not include any interaction with people, and we investigated how much information the proposed system successfully conveys by using a recall test and a questionnaire about the conversation and androids. The experimental results showed that participants who engaged with the proposed system recalled more content from the conversation and felt more empathic concern for androids. [ABSTRACT FROM AUTHOR]

The advantages of using a tele-operated robot to facilitate otherwise difficult verbal disclosure of thoughts and concerns was examined. In a field study, individuals with autism spectrum disorder, who often face obstacles in social interaction, were provided daily-life-guidance by a robot tele-operated by their caregiver. In cases in which the robot was operated remotely or from the same room, subjects were able to disclose concerns to the robot that they had not previously disclosed to their caregiver. Furthermore, under the latter case the improved communicability was maintained through subsequent conversation with the operator. In a separate laboratory experiment involving participants not judged to be on the autism spectrum, it was observed that the participants were silent longer during conversation via a robot than in face-to-face conversation. The enhanced length of silence in the first setting can be considered to be an index for good counseling of a subject. Furthermore, the enhanced length of silence was maintained in subsequent face-to-face conversation with a robot operator in the same room, which appears to be consistent with the results of the field experiment. The findings encourage future applications in therapeutic conversation approaches such as counseling for providing clients with environments that facilitate the disclosure of concerns. [ABSTRACT FROM AUTHOR]

We present KnoWDiaL, an approach for Learning and using task-relevant Knowledge from human-robot Dialog and access to the Web. KnoWDiaL assumes that there is an autonomous agent that performs tasks, as requested by humans through speech. The agent needs to “understand” the request, (i.e., to fully ground the task until it can proceed to plan for and execute it). KnoWDiaL contributes such understanding by using and updating a Knowledge Base, by dialoguing with the user, and by accessing the web. We believe that KnoWDiaL, as we present it, can be applied to general autonomous agents. However, we focus on our work with our autonomous collaborative robot, CoBot, which executes service tasks in a building, moving around and transporting objects between locations. Hence, the knowledge acquired and accessed consists of groundings of language to robot actions, and building locations, persons, and objects. KnoWDiaL handles the interpretation of voice commands, is robust regarding speech recognition errors, and is able to learn commands involving referring expressions in an open domain, (i.e., without requiring a lexicon). We present in detail the multiple components of KnoWDiaL, namely a frame-semantic parser, a probabilistic grounding model, a web-based predicate evaluator, a dialog manager, and the weighted predicate-based Knowledge Base. We illustrate the knowledge access and updates from the dialog and Web access, through detailed and complete examples. We further evaluate the correctness of the predicate instances learned into the Knowledge Base, and show the increase in dialog efficiency as a function of the number of interactions. We have extensively and successfully used KnoWDiaL in CoBot dialoguing and accessing the Web, and extract a few corresponding example sequences from captured videos.

Abstract This paper presents a multimodal human-robot interaction based on fusion of speech and gesture. In the interface, a robot control command system is designed, which can transform the speech and gesture of users into commands that the robot can execute. Microsoft speech SDK is used in this system to collect the speech of the operator. Then, a corpus-based algorithm of maximum entropy classification for natural language understanding is employed to generate commands. Leap Motion is employed to capture the gesture of operator in this system. Interval Kalman Filter (IKF) is used to estimate the measured data to reduce the inherent noise of the sensor. The advantage of the proposed method is that the combination of speech and gesture makes the human-robot interaction more convenient and direct. Finally, a series of experiments were carried out to validate our method, and proved that it performed better than the other proposed methods. [ABSTRACT FROM AUTHOR]

In recent years, there has been a growing interest in enabling autonomous social robots to interact with people. However, many questions remain unresolved regarding the social capabilities robots should have in order to perform this interaction in an ever more natural manner. In this paper, we tackle this problem through a comprehensive study of various topics involved in the interaction between a mobile robot and untrained human volunteers for a variety of tasks. In particular, this work presents a framework that enables the robot to proactively approach people and establish friendly interaction. To this end, we provided the robot with several perception and action skills, such as that of detecting people, planning an approach and communicating the intention to initiate a conversation while expressing an emotional status. We also introduce an interactive learning system that uses the person's volunteered assistance to incrementally improve the robot's perception skills. As a proof of concept, we focus on the particular task of online face learning and recognition. We conducted real-life experiments with our Tibi robot to validate the framework during the interaction process. Within this study, several surveys and user studies have been realized to reveal the social acceptability of the robot within the context of different tasks. [ABSTRACT FROM AUTHOR]

This conceptual paper revisits, refreshes and reinforces a 1984 study that challenged hospitality educators to include robotics in their classes and their research. The paper briefly reviews robotics literature, explains three robot categories-industrial, professional service and personal service- emphasises the importance of autonomy and human robot interaction, and provides hospitality and tourism examples. This literature review leads to six areas of importance for teaching and research of robotics in hospitality and tourism. The paper gives academics and practitioners a foundation for envisioning the current and future state of robots in hospitality and tourism. [ABSTRACT FROM AUTHOR]

The currently available speech technologies on mobile devices achieve effective performance in terms of both reliability and the language they are able to capture. The availability of performant speech recognition engines may also support the deployment of vocal interfaces in consumer robots. However, the design and implementation of such interfaces still requires significant work. The language processing chain and the domain knowledge must be built for the specific features of the robotic platform, the deployment environment and the tasks to be performed. Hence, such interfaces are currently built in a completely ad hoc way. In this paper, we present a design methodology together with a support tool aiming to streamline and improve the implementation of dedicated vocal interfaces for robots. This work was developed within an experimental project called Speaky for Robots. We extend the existing vocal interface development framework to target robotic applications. The proposed solution is built using a bottom-up approach by refining the language processing chain through the development of vocal interfaces for different robotic platforms and domains. The proposed approach is validated both in experiments involving several research prototypes and in tests involving end-users. [ABSTRACT FROM AUTHOR]

A pet cat or dog’s ability to respond to our emotional state opens an interaction channel with high visceral impact, which social robots may also be able to access. Touch is a key but understudied element; here, we explore its emotional content in the context of a furry robot pet. We asked participants to imagine feeling nine emotions located in a 2-D arousal-valence affect space, then to express them by touching a lap-sized robot prototype equipped with pressure sensors and accelerometer. We found overall correct classification (Random Forests) within the 2-D grid of 36% (all participants combined) and 48% (average of participants classified individually); chance 11%. Rates rose to 56% in the high arousal zone. To better understand classifier performance, we defined and analyzed new metrics that better indicate closeness of the gestural expressions. We also present a method to combine direct affect recognition with affect inferred from gesture recognition. This analysis provides a unique first insight into the nature and quality of affective touch, with implications as a design tool and for incorporating unintrusive affect sensing into deployed interactions. [ABSTRACT FROM AUTHOR]