To design trustworthy robots, we need to understand the impact factors of trust: people's attitudes, experience, and characteristics; the robot's physical design, reliability, and performance; a task's specification and the circumstances under which it is to be performed, e.g. at leisure or under time pressure. As robots are used for a wide variety of tasks and applications, robot designers ought to be provided with evidence and guidance, to inform their decisions to achieve safe, trustworthy and efficient human-robot interactions. In this work, the impact factors of trust in a collaborative manufacturing scenario are studied by conducting an experiment with a real robot and participants where a physical object was assembled and then disassembled. Objective and subjective measures were employed to evaluate the development of trust, under faulty and non-faulty robot conditions, and the effect of previous experience with robots, and personality traits. Our findings highlight differences when compared to other, more social, scenarios with robotic assistants (such as a home care assistant), in that the condition (faulty or not) does not have a significant impact on the human's perception of the robot in terms of human-likeliness, likeability, trustworthiness, and even competence. However, personality and previous experience do have an effect on how the robot is perceived by participants, even though that is relatively small.
As mentioned in @cite_31 , the ability of the humans (e.g. previous experience and training), their characteristics (e.g. demographics, personality, attitudes), the performance of the robot, and its attributed characteristics (e.g. anthropomorphism), as well as the environment (e.g. communication channels) and task nature (e.g. difficulty), have been studied as factors to contribute to the trust development in human--robot interactions. Also, a segment of research in human-robot interaction has focused on the influence of the robot's design (e.g. anthropomorphism, communication features, gestures) in the development of trust, as robot characteristics influence it the most, compared to environmental and human-related factors @cite_35 . @cite_7 and @cite_29 have studied whether people trust expressive robots more or not, even when they make mistakes. These studies found that co-verbal gestures and more expressiveness caused the robots to be perceived more human-like and likeable, increasing future contact or interaction intentions, even though the mistakes affected a successful performance during the tasks.
@cite_35 @cite_31 @cite_29 @cite_7
2132244529 2950510190 1566550256
Previous work has shown that gestural behaviors affect anthropomorphic inferences about artificial communicators such as virtual agents. In an experiment with a humanoid robot, we investigated to what extent gesture would affect anthropomorphic inferences about the robot. Particularly, we examined the effects of the robot's hand and arm gestures on the attribution of typically human traits, likability of the robot, shared reality, and future contact intentions after interacting with the robot. For this, we manipulated the non-verbal behaviors of the humanoid robot in three experimental conditions: (1) no gesture, (2) congruent gesture, and (3) incongruent gesture. We hypothesized higher ratings on all dependent measures in the two gesture (vs. no gesture) conditions. The results confirm our predictions: when the robot used gestures during interaction, it was anthropomorphized more, participants perceived it as more likable, reported greater shared reality with it, and showed increased future contact intentions than when the robot gave instructions without using gestures. Surprisingly, this effect was particularly pronounced when the robot's gestures were partly incongruent with speech. These findings show that communicative non-verbal behaviors in robotic systems affect both anthropomorphic perceptions and the mental models humans form of a humanoid robot during interaction. Future applications for personal robots motivate research into developing robots that are intelligent in their interactions with people. Toward this goal, in this paper we present an integrated socio-cognitive architecture to endow an anthropomorphic robot with the ability to infer mental states such as beliefs, intents, and desires from the observable behavior of its human partner. The design of our architecture is informed by recent findings from neuroscience and embodies cognition that reveals how living systems leverage their physical and cognitive embodiment through simulation-theoretic mechanisms to infer the mental states of others. We assess the robot's mindreading skills on a suite of benchmark tasks where the robot interacts with a human partner in a cooperative scenario and a learning scenario. In addition, we have conducted human subjects experiments using the same task scenarios to assess human performance on these tasks and to compare the robot's performance with that of people. In the process, our human subject studies also reveal some interesting insights into human behavior. When people interact with communication robots in daily life, their attitudes and emotions toward the robots affect their behavior. From the perspective of robotics design, we need to investigate the influences of these attitudes and emotions on human-robot interaction. This paper reports our empirical study on the relationships between people's attitudes and emotions, and their behavior toward a robot. In particular, we focused on negative attitudes, anxiety, and communication avoidance behavior, which have important implications for robotics design. For this purpose, we used two psychological scales that we had developed: negative attitudes toward robots scale (NARS) and robot anxiety scale (RAS). In the experiment, subjects and a humanoid robot are engaged in simple interactions including scenes of meeting, greeting, self-disclosure, and physical contact. Experimental results indicated that there is a relationship between negative attitudes and emotions, and communication avoidance behavior. A gender effect was also suggested. Throughout the history of automation, there have been numerous accidents attributed to the inappropriate use of automated systems. Over-reliance and under-reliance on automation are well documented problems in fields where automation has been employed for a long time, such as factories and aviation. Research has shown that one of the key factors that influence an operator's reliance on automated systems is his or her trust of the system. Several factors, including risk, workload, and task difficulty have been found to influence an operator's trust of an automated system. With a model of trust based upon these factors, it is possible to design automated systems that foster well-calibrated trust and thereby prevent the misuse of automation. Over the past decade, robot systems have become more commonplace and increasingly autonomous. With the increased use of robot systems in multiple application domains, models of trust and operator behavior for human-robot interaction (HRI) must be created now in order to avoid some of the problems encountered by other automation domains in the past. Since traditional automation domains and HRI are significantly different, we reexamine trust and control allocation (operator's usage of autonomous behaviors) as it relates to robots with autonomous capabilities in order to discover the relevant factors in HRI. This dissertation examines existing work in traditional automation that is relevant to HRI and, based on that information, builds an experimental methodology to closely mimic real world remote robot teleoperation tasks. We also present results from multiple experiments examining the relationship between the different factors being investigated with respect to trust and control allocation. Based on these results, a model for human interaction with remote robots for teleoperation (HARRT) is proposed and design guidelines to help improve overall performance are presented based on the model. In order to keep healthy health of elderly people, daily exercise is an important factor. Therefore, we have developed an exercise support system with robot partner to provide the daily exercise program. Furthermore, Human-like-motion can have a contribution to exert a strong influence on the person through the human-robot interaction, because bodily expressions convey important and effective information. If robots could adapt the features of human behavior to their motions and skills, the communication would become more smooth and natural. In this paper, we propose a learning structure for imitation learning. As robots enter the everyday physical world of people, it is important that they abide by society's unspoken social rules such as respecting people's personal spaces. In this paper, we explore issues related to human personal space around robots, beginning with a review of the existing literature in human-robot interaction regarding the dimensions of people, robots, and contexts that influence human-robot interactions. We then present several research hypotheses which we tested in a controlled experiment (N=30). Using a 2 (robotics experience vs. none: between-participants) × 2 (robot head oriented toward a participant's face vs. legs: within-participants) mixed design experiment, we explored the factors that influence proxemic behavior around robots in several situations: (1) people approaching a robot, (2) people being approached by an autonomously moving robot, and (3) people being approached by a teleoperated robot. We found that personal experience with pets and robots decreases a person's personal space around robots. In addition, when the robot's head is oriented toward the person's face, it increases the minimum comfortable distance for women, but decreases the minimum comfortable distance for men. We also found that the personality trait of agreeableness decreases personal spaces when people approach robots, while the personality trait of neuroticism and having negative attitudes toward robots increase personal spaces when robots approach people. These results have implications for both human-robot interaction theory and design. Strategies are necessary to mitigate the impact of unexpected behavior in collaborative robotics, and research to develop solutions is lacking. Our aim here was to explore the benefits of an affective interaction, as opposed to a more efficient, less error prone but non-communicative one. The experiment took the form of an omelet-making task, with a wide range of participants interacting directly with BERT2, a humanoid robot assistant. Having significant implications for design, results suggest that efficiency is not the most important aspect of performance for users; a personable, expressive robot was found to be preferable over a more efficient one, despite a considerable trade off in time taken to perform the task. Our findings also suggest that a robot exhibiting human-like characteristics may make users reluctant to 'hurt its feelings'; they may even lie in order to avoid this. Since a variety of changes in both robotic hardware and software suggests that service robots will soon become possible, to find "natural" ways of communication between human and robots is of fundamental importance for the robotic field. The paper describes a gesture-based interface for human-robot interaction, which enables people to instruct robots through easy-to-perform arm gestures. Such gestures might be static pose gestures, which involve only a specific configuration of the person's arm, or they might be dynamic motion gestures, that is, they involve motion (such as waving). Gestures are recognized in real-time at approximate frame rate, using neural networks. A fast, color-based tracking algorithm enables the robot to track and follow a person reliably through office environments with drastically changing lighting conditions. Results are reported in the context of an interactive clean-up task, where a person guides the robot to specific locations that need to be cleaned, and the robot picks up trash which it then delivers to the nearest trash-bin. The animation techniques of anticipation and reaction can help create robot behaviors that are human readable such that people can figure out what the robot is doing, reasonably predict what the robot will do next, and ultimately interact with the robot in an effective way. By showing forethought before action and expressing a reaction to the task outcome (success or failure), we prototyped a set of human-robot interaction behaviors. In a 2 (forethought vs. none: between) x 2 (reaction to outcome vs. none: between) x 2 (success vs. failure task outcome: within) experiment, we tested the influences of forethought and reaction upon people's perceptions of the robot and the robot's readability. In this online video prototype experiment (N=273), we have found support for the hypothesis that perceptions of robots are influenced by robots showing forethought, the task outcome (success or failure), and showing goal-oriented reactions to those task outcomes. Implications for theory and design are discussed. In all human-robot interaction, trust is an important element to consider because the presence or absence of trust certainly impacts the ultimate outcome of that interaction. Limited research exists that delineates the development and maintenance of this trust in various operational contexts. Our own prior research has investigated theoretical and empirically supported antecedents of human-robot trust. Here, we describe progress to date relating to the development of a comprehensive human-robot trust model based on our ongoing program of research. Abstract Recently, the concept of human-robot collaboration has raised many research interests. Instead of robots replacing human workers in workplaces, human-robot collaboration allows human workers and robots working together in a shared manufacturing environment. Human-robot collaboration can release human workers from heavy tasks with assistive robots if effective communication channels between humans and robots are established. Although the communication channels between human workers and robots are still limited, gesture recognition has been effectively applied as the interface between humans and computers for long time. Covering some of the most important technologies and algorithms of gesture recognition, this paper is intended to provide an overview of the gesture recognition research and explore the possibility to apply gesture recognition in human-robot collaborative manufacturing. In this paper, an overall model of gesture recognition for human-robot collaboration is also proposed. There are four essential technical components in the model of gesture recognition for human-robot collaboration: sensor technologies, gesture identification, gesture tracking and gesture classification. Reviewed approaches are classified according to the four essential technical components. Statistical analysis is also presented after technical analysis. Towards the end of this paper, future research trends are outlined. In an experiment we manipulated a robot's voice in two ways: First, we varied robot gender; second, we equipped the robot with a human-like or a robot-like synthesized voice. Moreover, we took into account user gender and tested effects of these factors on human-robot acceptance, psychological closeness and psychological anthropomorphism. When participants formed an impression of a same-gender robot, the robot was perceived more positively. Participants also felt more psychological closeness to the same-gender robot. Similarly, the same-gender robot was anthropomorphized more strongly, but only when it utilized a human-like voice. Results indicate that a projection mechanism could underlie these effects. This paper presents a machine vision based ap- proach for human operators to select individual and groups of autonomous robots from a swarm of UAVs. The angular distance between the robots and the human is estimated using measures of the detected human face, which aids to determine human and multi-UAV localization and positioning. In turn, this is exploited to effectively and naturally make the human select the spatially situated robots. Spatial gestures for selecting robots are presented by the human operator using tangible input devices (i.e., colored gloves). To select individuals and groups of robot we formulate a vocabulary of two-handed spatial pointing gestures. With the use of a Support Vector Machine (SVM) trained in a cascaded multi-binary-class configuration, the spatial gestures are effectively learned and recognized by a swarm of UAVs. I. INTRODUCTION Without the use of teleoperated and hand-held interaction devices, human operators generally face difficulties in select- ing and commanding individual and groups of robots from a relatively large group of spatially distributed robots (i.e., a swarm). However, due to the widespread availability of cost effective digital cameras onboard UGVs and UAVs, it is increasing the attention towards developing uninstrumented methods (i.e., methods that do not use sophisticated hardware devices from the human side) for human-swarm interaction (HSI). In previous work, we focused on learning efficient features incrementally (online) from multi-viewpoint images of multiple gestures that were acquired by a swarm of ground robots (1). In this paper, we present a cascaded supervised machine learning approach to deal with the machine vision problem of selecting 3D spatially-situated robots from a networked swarm based on the recognition of spatial hand gestures. These are a natural, easy recognizable, and device- less way to enable human operators to easily interact with external artifacts such as robots. Inspired by natural human behavior, we propose an ap- proach that combines face engagement and pointing gestures to interact with a swarm of robots: standing in front of a population of robots, by looking at them and pointing at them with spatial gestures, a human operator can designate individual or groups of robots of determined size. Robots cooperate to combine their independent observations of the human's face and gestures to cooperatively determine which robots were addressed (i.e., selected). While state of the art computer vision techniques pro- vide excellent face detection, human skeleton, and gesture recognition in ideal conditions, there are often occlusions, When humans and mobile robots share the same space, one of their challenges is to navigate around each other and manage their mutual navigational intents. While humans have developed excellent skills in inferring their counterpart's intentions via a number of implicit and non-verbal cues, making navigation also in crowds an ease, this kind of effective and efficient communication often falls short in human-robot encounters. In this paper, two alternative approaches to convey navigational intent of a mobile robot to humans in a shared environment are proposed and analysed. The first is utilising anthropomorphic features of the mobile robot to realise an implicit joint attention using gaze to represent the direction of navigational intent. In the second approach, a more technical design adopting the semantics of car's turn indicators, has been implemented. The paper compares both approaches with each other and against a control behaviour without any communication of intent. Both approaches show statistically significant differences in comparison to the control behaviour. However, the second approach using indicators has shown as being more effective in conveying the intent and also has a higher positive impact on the comfort of the humans encountering the robot. Nonverbal communication cues play an important role in human-human interaction and are expected to take a similar role in human-robot collaboration. In current industrial practice, human-robot turn-taking is explicitly human controlled, via a command channel such as switch or button. However, such a master-slave approach does not permit collaborative interaction, and requires the human to focus on both controlling the robot's behavior and on the task, thereby affecting overall performance. In this paper, implicit, nonverbal communication cues are examined as a non-explicit communication channel during a turn-taking task context. The aim of this study is to characterize the types and frequencies of nonverbal cues important to regulating turn taking during an assembly-task-type collaboration. This analysis will guide the selection of cues that can be expressed by the robot as implicit user inputs while human and robot complete a shared task. We describe a model of “trust” in human-robot systems that is inferred from their interactions, and inspired by similar concepts relating to trust among humans. This computable quantity allows a robot to estimate the extent to which its performance is consistent with a human's expectations, with respect to task demands. Our trust model drives an adaptive mechanism that dynamically adjusts the robot's autonomous behaviors, in order to improve the efficiency of the collaborative team. We illustrate this trust-driven methodology through an interactive visual robot navigation system. This system is evaluated through controlled user experiments and a field demonstration using an aerial robot. The Social Mediator forum was created to bridge the gaps between the theory and practice of social media research and development. The articles are intended to promote greater awareness of new insights and experiences in the rapidly evolving domain of social media, some of which may influence perspectives and approaches in the more established areas of human-computer interaction. Each article in the forum is made up of several short contributions from people representing different perspectives on a particular topic. Previous installments of this forum have woven together diverse perspectives on the ways that social media is transforming relationships among different stakeholders in the realms of healthcare and government. The current article highlights some of the ways social robots (socialbots)---programs that operate autonomously on social networking sites---are transforming relationships within those sites, and how these transformations may more broadly influence relationships among people and organizations in the future. A recent article in Communications of the ACM called "The Social Life of Robots" reported that "researchers have started to explore the possibilities of 'social' machines capable of working together with minimal human supervision" [1]. That article illuminates recent developments involving interactions between humans and robots in the physical world; this article focuses on the interactions between humans and robots in the virtual world. Our authors are exploring and expanding the frontiers of designing, deploying, and analyzing the behavior and impact of robots operating in online social networks, and they have invited a number of other frontierspeople to share some of their insights, experiences, and future expectations for social robotics. We design and evaluate a method of human-robot cross-training, a validated and widely used strategy for the effective training of human teams. Cross-training is an interactive planning method in which team members iteratively switch roles with one another to learn a shared plan for the performance of a collaborative task. We first present a computational formulation of the robot mental model, which encodes the sequence of robot actions necessary for task completion and the expectations of the robot for preferred human actions, and show that the robot model is quantitatively comparable to the mental model that captures the inter-role knowledge held by the human. Additionally, we propose a quantitative measure of robot mental model convergence and an objective metric of model similarity. Based on this encoding, we formulate a human-robot cross-training method and evaluate its efficacy through experiments involving human subjects n = 60 . We compare human-robot cross-training to standard reinforcement learning techniques, and show that cross-training yields statistically significant improvements in quantitative team performance measures, as well as significant differences in perceived robot performance and human trust. Finally, we discuss the objective measure of robot mental model convergence as a method to dynamically assess human errors. This study supports the hypothesis that the effective and fluent teaming of a human and a robot may best be achieved by modeling known, effective human teamwork practices. The study presented in this paper explored people's perceptions and attitudes towards the idea of a future robot companion for the home. A human-centred approach was adopted using questionnaires and human-robot interaction trials to derive data from 28 adults. Results indicated that a large proportion of participants were in favour of a robot companion and saw the potential role as being an assistant, machine or servant. Few wanted a robot companion to be a friend. Household tasks were preferred to child animal care tasks. Humanlike communication was desirable for a robot companion, whereas humanlike behaviour and appearance were less essential. Results are discussed in relation to future research directions for the development of robot companions. An objective of natural Human-Robot Interaction (HRI) is to enable humans to communicate with robots in the same manner humans do between themselves. This includes the use of natural gestures to support and expand the information that is exchanged in the spoken language. To achieve that, robots need robust gesture recognition systems to detect the non-verbal information that is sent to them by the human gestures. Traditional gesture recognition systems highly depend on the light conditions and often require a training process before they can be used. We have integrated a low-cost commercial RGB-D (Red Green Blue - Depth) sensor in a social robot to allow it to recognise dynamic gestures by tracking a skeleton model of the subject and coding the temporal signature of the gestures in a FSM (Finite State Machine). The vision system is independent of low light conditions and does not require a training process.