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.
Other scales measure people's perceptions of robots during an interaction, e.g. in @cite_6 @cite_8 , which studied the effect of people's general attitudes and emotions such as technophobia or social anxiety on the interaction outcomes. Nonetheless, solely using questionnaires and scales make these metrics entirely subjective, not considering factors such as the presence of faults in the robot system. Ideally, both objective and subjective metrics should be added to a study @cite_9 .
@cite_9 @cite_6 @cite_8
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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. This paper describes the use of the Negative Attitudes Towards Robots Scale (NARS) to explain participants' evaluations of robot behaviour styles in a Human-Robot Interaction (HRI) study. Twentyeight participants interacted with a robot in two experimental conditions in which the robot’s behaviour was varied. Reliability analysis and a PCA was performed on the NARS items, creating three new subscales. Correlations between the subscales and other evaluations of the robot's behaviour found meaningful results, supporting the use of the NARS in English speaking samples. 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. This article presents the results of video-based Human Robot Interaction (HRI) trials which investigated people's perceptions of different robot appearances and associated attention-seeking features and behaviors displayed by robots with different appearance and behaviors. The HRI trials studied the participants' preferences for various features of robot appearance and behavior, as well as their personality attributions towards the robots compared to their own personalities. Overall, participants tended to prefer robots with more human-like appearance and attributes. However, systematic individual differences in the dynamic appearance ratings are not consistent with a universal effect. Introverts and participants with lower emotional stability tended to prefer the mechanical looking appearance to a greater degree than other participants. It is also shown that it is possible to rate individual elements of a particular robot's behavior and then assess the contribution, or otherwise, of that element to the overall perception of the robot by people. Relating participants' dynamic appearance ratings of individual robots to independent static appearance ratings provided evidence that could be taken to support a portion of the left hand side of Mori's theoretically proposed uncanny valley' diagram. Suggestions for future work are outlined. Affect sensitivity is of the utmost importance for a robot companion to be able to display socially intelligent behaviour, a key requirement for sustaining long-term interactions with humans. This paper explores a naturalistic scenario in which children play chess with the iCat, a robot companion. A person-independent, Bayesian approach to detect the user's engagement with the iCat robot is presented. Our framework models both causes and effects of engagement: features related to the user's non-verbal behaviour, the task and the companion's affective reactions are identified to predict the children's level of engagement. An experiment was carried out to train and validate our model. Results show that our approach based on multimodal integration of task and social interaction-based features outperforms those based solely on non-verbal behaviour or contextual information (94.79 vs. 93.75 and 78.13 ). In order for humans and robots to interact in an effective and intuitive manner, robots must obtain information about the human affective state in response to the robot's actions. This secondary mode of interactive communication is hypothesized to permit a more natural collaboration, similar to the "body language" interaction between two cooperating humans. This paper describes the implementation and validation of a hidden Markov model (HMM) for estimating human affective state in real time, using robot motions as the stimulus. Inputs to the system are physiological signals such as heart rate, perspiration rate, and facial muscle contraction. Affective state was estimated using a two- dimensional valence-arousal representation. A robot manipulator was used to generate motions expected during human-robot interaction, and human subjects were asked to report their response to these motions. The human physiological response was also measured. Robot motions were generated using both a nominal potential field planner and a recently reported safe motion planner that minimizes the potential collision forces along the path. The robot motions were tested with 36 subjects. This data was used to train and validate the HMM model. The results of the HMM affective estimation are also compared to a previously implemented fuzzy inference engine. 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. All currently used mobile robot platforms are able to navigate safely through their environment, avoiding static and dynamic obstacles. However, in human populated environments mere obstacle avoidance is not sufficient to make humans feel comfortable and safe around robots. To this end, a large community is currently producing human-aware navigation approaches to create a more socially acceptable robot behaviour. Amajorbuilding block for all Human-Robot Spatial Interaction is the ability of detecting and tracking humans in the vicinity of the robot. We present a fully integrated people perception framework, designed to run in real-time on a mobile robot. This framework employs detectors based on laser and RGB-D data and a tracking approach able to fuse multiple detectors using different versions of data association and Kalman filtering. The resulting trajectories are transformed into Qualitative Spatial Relations based on a Qualitative Trajectory Calculus, to learn and classify different encounters using a Hidden Markov Model based representation. We present this perception pipeline, which is fully implemented into the Robot Operating System (ROS), in a small proof of concept experiment. All components are readily available for download, and free to use under the MIT license, to researchers in all fields, especially focussing on social interaction learning by providing different kinds of output, i.e. Qualitative Relations and trajectories. Objective:The objective of this work was to examine human response to motion-level robot adaptation to determine its effect on team fluency, human satisfaction, and perceived safety and comfort.Background:The evaluation of human response to adaptive robotic assistants has been limited, particularly in the realm of motion-level adaptation. The lack of true human-in-the-loop evaluation has made it impossible to determine whether such adaptation would lead to efficient and satisfying human–robot interaction.Method:We conducted an experiment in which participants worked with a robot to perform a collaborative task. Participants worked with an adaptive robot incorporating human-aware motion planning and with a baseline robot using shortest-path motions. Team fluency was evaluated through a set of quantitative metrics, and human satisfaction and perceived safety and comfort were evaluated through questionnaires.Results:When working with the adaptive robot, participants completed the task 5.57 faster, with 19.9... Humans are capable of manipulating objects based solely on the sense of touch. For robots to achieve the same feat in unstructured environments, global localization of objects via touch is required. Bayesian approaches provide the means to cope with uncertainties of the real world, but the estimation of the Bayesian posterior for the full six degrees of freedom (6-DOF) global localization problem is computationally prohibitive. We propose an efficient Bayesian approach termed Scaling Series. It is capable of solving the full problem reliably in real time. This is a Monte Carlo approach that performs a series of successive refinements coupled with annealing. We also propose an analytical measurement model, which can be computed efficiently at run time for any object represented as a polygonal mesh. Extensive empirical evaluation shows that Scaling Series drastically outperforms prior approaches. We demonstrate general applicability of the approach on five common solid objects, which are rigidly fixed during the experiments. We also consider 6-DOF localization and tracking of free-standing objects that can move during tactile exploration. Fetching items is an important problem for a social robot. It requires a robot to interpret a person's language and gesture and use these noisy observations to infer what item to deliver. If the robot could ask questions, it would help the robot be faster and more accurate in its task. Existing approaches either do not ask questions, or rely on fixed question-asking policies. To address this problem, we propose a model that makes assumptions about cooperation between agents to perform richer signal extraction from observations. This work defines a mathematical framework for an item-fetching domain that allows a robot to increase the speed and accuracy of its ability to interpret a person's requests by reasoning about its own uncertainty as well as processing implicit information (implicatures). We formalize the item-delivery domain as a Partially Observable Markov Decision Process (POMDP), and approximately solve this POMDP in real time. Our model improves speed and accuracy of fetching tasks by asking relevant clarifying questions only when necessary. To measure our model's improvements, we conducted a real world user study with 16 participants. Our method achieved greater accuracy and a faster interaction time compared to state-of-the-art baselines. Our model is 2.17 seconds faster (25 faster) than a state-of-the-art baseline, while being 2.1 more accurate. Our goal is to build robots that can robustly interact with humans using natural language. This problem is challenging because human language is filled with ambiguity, and furthermore, due to limitations in sensing, the robot's perception of its environment might be much more limited than that of its human partner. To enable a robot to recover from a failure to understand a natural language utterance, this paper describes an information-theoretic strategy for asking targeted clarifying questions and using information from the answer to disambiguate the language. To identify good questions, we derive an estimate of the robot's uncertainty about the mapping between specific phrases in the language and aspects of the external world. This metric enables the robot to ask a targeted question about the parts of the language for which it is most uncertain. After receiving an answer, the robot fuses information from the command, the question, and the answer in a joint probabilistic graphical model in the G3 framework. When using answers to questions, we show the robot is able to infer mappings between parts of the language and concrete object groundings in the external world with higher accuracy than by using information from the command alone. Furthermore, we demonstrate that by effectively selecting which questions to ask, the robot is able to achieve significant performance gains while asking many fewer questions than baseline metrics. Here, we propose that bidirectionality in implicit motor coordination between humanoid robots and humans could enhance the social competence of human–robot interactions. We first detail some questions pertaining to human–robot interactions, introducing the Uncanny Valley hypothesis. After introducing a framework pertinent for the understanding of natural social interactions, motor resonance, we examine two behaviors derived from this framework: motor coordination, investigated in and informative about human–human interaction, and motor interference, which demonstrate the relevance of the motor resonance framework to describe human perception of humanoid robots. These two lines of investigation are then put together to “close the loop” by proposing to implement a key feature of motor coordination, bidirectionality, in robots’ behavior. Finally, we discuss the feasibility of implementing motor coordination between humanoid robots and humans, and the consequences of this implementation in enhancing the social competence of robots interacting with humans. Keywords: interpersonal interaction, motor resonance, motor coordination, motor interference, social robotics, anthropomorphism HRI researchers interested in social robots have made large investments in humanoid robots. There is still sparse evidence that peoples' responses to robots differ from their responses to computer agents, suggesting that agent studies might serve to test HRI hypotheses. To help us understand the difference between people's social interactions with an agent and a robot, we experimentally compared people's responses in a health interview with (a) a computer agent projected either on a computer monitor or life-size on a screen, (b) a remote robot projected life-size on a screen, or (c) a collocated robot in the same room. We found a few behavioral and large attitude differences across these conditions. Participants forgot more and disclosed least with the collocated robot, next with the projected remote robot, and then with the agent. They spent more time with the collocated robot and their attitudes were most positive toward that robot. We discuss tradeoffs for HRI research of using collocated robots, remote robots, and computer agents as proxies of robots. Due to a major shortage of nurses in the U.S., future healthcare service robots are expected to be used in tasks involving direct interaction with patients. Consequently, there is a need to design nursing robots with the capability to detect and respond to patient emotional states and to facilitate positive experiences in healthcare. The objective of this study was to develop a new computational algorithm for accurate patient emotional state classification in interaction with nursing robots during medical service. A simulated medicine delivery experiment was conducted at two nursing homes using a robot with different human-like features. Physiological signals, including heart rate (HR) and galvanic skin response (GSR), as well as subjective ratings of valence (happy-unhappy) and arousal (excited-bored) were collected on elderly residents. A three-stage emotional state classification algorithm was applied to these data, including: (1) physiological feature extraction; (2) statistical-based feature selection; and (3) a machine-learning model of emotional states. A pre-processed HR signal was used. GSR signals were nonstationary and noisy and were further processed using wavelet analysis. A set of wavelet coefficients, representing GSR features, was used as a basis for current emotional state classification. Arousal and valence were significantly explained by statistical features of the HR signal and GSR wavelet features. Wavelet-based de-noising of GSR signals led to an increase in the percentage of correct classifications of emotional states and clearer relationships among the physiological response and arousal and valence. The new algorithm may serve as an effective method for future service robot real-time detection of patient emotional states and behavior adaptation to promote positive healthcare experiences. The ability to act in a socially-aware way is a key skill for robots that share a space with humans. In this paper we address the problem of socially-aware navigation among people that meets objective criteria such as travel time or path length as well as subjective criteria such as social comfort. Opposed to model-based approaches typically taken in related work, we pose the problem as an unsupervised learning problem. We learn a set of dynamic motion prototypes from observations of relative motion behavior of humans found in publicly available surveillance data sets. The learned motion prototypes are then used to compute dynamic cost maps for path planning using an any-angle A* algorithm. In the evaluation we demonstrate that the learned behaviors are better in reproducing human relative motion in both criteria than a Proxemics-based baseline method. Wagstaff (2012) draws attention to the pervasiveness of abstract evaluation metrics that explicitly ignore or remove problem specifics. While such metrics allow practitioners to compare numbers across application domains, they offer limited insight into the impact of algorithmic decisions on humans and their perception of the algorithm's correctness. Even for problems that are mathematically the same, both the real-cost of (mathematically) identical errors, as well as their perceived-cost by users, may significantly vary according to the specifics of each problem domain, as well as of the user perceiving the result. While the real-cost of errors has been considered previously, little attention has been paid to the perceived-cost issue. We advocate for the inclusion of human-centered metrics that elicit error costs from humans from two perspectives: the nature of the error, and the user context. Focusing on hate speech detection on social media, we demonstrate that even when fixing the performance as measured by an abstract metric such as precision, user perception of correctness varies greatly depending on the nature of errors and user characteristics. This paper studies how an operator with limited situational awareness can collaborate with a swarm of simulated robots. The robots are distributed in an environment with wall obstructions. They aggregate autonomously but are unable to form a single cluster due to the obstructions. The operator lacks the bird’s-eye perspective, but can interact with one robot at a time, and influence the behavior of other nearby robots. We conducted a series of experiments. They show that untrained participants had marginal influence on the performance of the swarm. Expert participants succeeded in aggregating 85 of the robots while untrained participants, with bird’s-eye view, succeeded in aggregating 90 . This demonstrates that the controls are sufficient for operators to aid the autonomous robots in the completion of the task and that lack of situational awareness is the main difficulty. An analysis of behavioral differences reveals that trained operators learned to gain superior situational awareness. In robotics, the use of a classification framework which produces scores with inappropriate confidences will ultimately lead to the robot making dangerous decisions. In order to select a framework which will make the best decisions, we should pay careful attention to the ways in which it generates scores. Precision and recall have been widely adopted as canonical metrics to quantify the performance of learning algorithms, but for robotics applications involving mission-critical decision making, good performance in relation to these metrics is insufficient. We introduce and motivate the importance of a classifier's introspective capacity: the ability to associate an appropriate assessment of confidence with any test case. We propose that a key ingredient for introspection is a framework's potential to increase its uncertainty with the distance between a test datum its training data. We compare the introspective capacities of a number of commonly used classification frameworks in both classification and detection tasks, and show that better introspection leads to improved decision making in the context of tasks such as autonomous driving or semantic map generation. This paper presents four exploratory studies of the potential use of robots for gathering customer feedback in the hospitality industry. To account for the viewpoints of both hotels and guests, we administered need finding interviews at five hotels and an online survey concerning hotel guest experiences with 60 participants. We then conducted the two deployment studies based on deploying software prototypes for Savioke Relay robots we designed to collect customer feedback: (i) a hotel deployment study (three hotels over three months) to explore the feasibility of robot use for gathering customer feedback as well as issues such deployment might pose and (ii) a hotel kitchen deployment study (at Savioke headquarters over three weeks) to explore the role of different robot behaviors (mobility and social attributes) in gathering feedback and understand the customers' thought process in the context that they experience a service. We found that hotels want to collect customer feedback in real-time to disseminate positive feedback immediately and to respond to unhappy customers while they are still on-site. Guests want to inform the hotel staff about their experiences without compromising their convenience and privacy. We also found that the robot users, e.g. hotel staff, use their domain knowledge to increase the response rate to customer feedback surveys at the hotels. Finally, environmental factors, such as robot's location in the building influenced customer response rates more than altering the behaviors of the robot collecting the feedback.