Context Of Human-robot Interaction Research Articles

Sonic Sleight of Hand: Sound Induces Illusory Distortions in the Perception and Prediction of Robot Action

AbstractFor efficient human–robot interaction, human operators need to be able to efficiently represent the robot’s movements in space and predict its next steps. However, according to frameworks of Bayesian multisensory integration, features outside the motion itself—like the sounds a robot makes while it moves—should affect how otherwise identical motions are perceived. Here, we translate an established psychophysical task from experimental psychology to a human–robot interaction context, which can measure these distortions to motion perception. In two series of preregistered studies, participants watched a humanoid robot make forward and backward reaching movements. When the robot hand suddenly disappeared, they reported its last seen location, either with the mouse cursor (Experiment 1a and 1b) or by matching it to probe stimuli in different locations (Experiment 2a and 2b). The results revealed that even small changes to the robot’s sound robustly affect participants’ visuospatial representation of its motions, so that the motion appeared to extend further in space when accompanied by slightly (100 ms) longer sounds compared to slightly shorter sounds (100 ms shorter). Moreover, these sound changes do not only affect where people currently locate the robot’s motion, but where they anticipate its future steps. These findings show that sound design is an effective medium for manipulating how people represent otherwise identical robot actions and coordinate its interactions with it. The study acts as proof of concept that psychophysical tasks provide a promising tool to measure how design parameters influence the perception and prediction of robot motion.

Models of Human Behavior for Human-Robot Interaction and Automated Driving: How Accurate Do the Models of Human Behavior Need to Be?

There are many examples of cases where access to improved models of human behavior and cognition has allowed creation of robots which can better interact with humans, and not least in road vehicle automation this is a rapidly growing area of research. Human-robot interaction (HRI) therefore provides an important applied setting for human behavior modeling - but given the vast complexity of human behavior, how complete and accurate do these models need to be? Here, we outline some possible ways of thinking about this problem, starting from the suggestion that modelers need to keep the right end goal in sight: A successful human-robot interaction, in terms of safety, performance, and human satisfaction. Efforts toward model completeness and accuracy should be focused on those aspects of human behavior to which interaction success is most sensitive. We emphasise that identifying which those aspects are is a difficult scientific objective in its own right, distinct for each given HRI context. We propose and exemplify an approach to formulating a priori hypotheses on this matter, in cases where robots are to be involved in interactions which currently take place between humans, such as in automated driving. Our perspective also highlights some possible risks of overreliance on machine-learned models of human behavior in HRI, and how to mitigate against those risks.

A Human Error Analysis in Human–Robot Interaction Contexts: Evidence from an Empirical Study

More than 60 years has passed since the installation of the first robot in an industrial context. Since then, industrial robotics has seen great advancements and, today, robots can collaborate with humans in executing a wide range of working activities. Nevertheless, the impact of robots on human operators has not been deeply investigated. To address this problem, we conducted an empirical study to measure the errors performed by two groups of people performing a working task through a virtual reality (VR) device. A sample of 78 engineering students participated in the experiments. The first group worked with a robot, sharing the same workplace, while the second group worked without the presence of a robot. The number of errors made by the participants was collected and analyzed. Although statistical results show that there are no significant differences between the two groups, qualitative analysis proves that the presence of the robot led to people paying more attention during the execution of the task, but to have a worse learning experience.

“It's not Paul, it's a robot”: The impact of linguistic framing and the evolution of trust and distrust in a collaborative robot during a human-robot interaction

An appropriate level of human-robot trust is widely acknowledged as a crucial factor for successful human-robot interaction (HRI). Previous research has addressed anthropomorphic design but mostly neglected linguistic framing as a means to influence trust by altering the perceived human-likeness of a robot. Similarly, time-dependant patterns of trust and distrust during an HRI have rarely been investigated, hindering the development of a coherent theoretical framework on framing effects, their formation conditions, and their relation to trust and distrust evolution in the HRI context. A previous online study suggested that linguistic framing modulates inexperienced factory workers’ trust in a collaborative robot at the workplace. This article presents a follow-up study with a sample of students and an realistic HRI setting. Despite using similar framing stimuli, the study failed to replicate significant framing effects on trust, suggesting their context-sensitivity and dependence on individual characteristics of the recipients of the framing. Additionally, a significant increase in trust and decrease in distrust during the HRI highlights the need for time-dependant considerations. The results call for a more fine-grained investigation of linguistic framing effects as well as of trust and distrust evolution in order to develop a comprehensive theoretical framework applicable to the HRI context.

Neuromorphic Computing for Interactive Robotics: A Systematic Review

Modelling functionalities of the brain in human-robot interaction contexts requires a real-time understanding of how each part of a robot (motors, sensors, emotions, etc.) works and how they interact all together to accomplish complex behavioural tasks while interacting with the environment. Human brains are very efficient as they process the information using event-based impulses also known as spikes, which make living creatures very efficient and able to outperform current mainstream robotic systems in almost every task that requires real-time interaction. In recent years, combined efforts by neuroscientists, biologists, computer scientists and engineers make it possible to design biologically realistic hardware and models that can endow the robots with the required human-like processing capability based on neuromorphic computing and Spiking Neural Network (SNN). However, while some attempts have been made, a comprehensive combination of neuromorphic computing and robotics is still missing. In this article, we present a systematic review of neuromorphic computing applications for socially interactive robotics.We first introduce the basic principles, models and architectures of neuromorphic computation. The remaining articles are classified according to the applications they focus on. Finally, we identify the potential research topics for fully integrated socially interactive neuromorphic robots.

Exploring Behavioral Creativity of a Proactive Robot.

Creativity, in one sense, can be seen as an effort or action to bring novelty. Following this, we explore how a robot can be creative by bringing novelty in a human–robot interaction (HRI) scenario. Studies suggest that proactivity is closely linked with creativity. Proactivity can be defined as acting or interacting by anticipating future needs or actions. This study aims to explore the effect of proactive behavior and the relation of such behaviors to the two aspects of creativity: 1) the perceived creativity observed by the user in the robot’s proactive behavior and 2) creativity of the user by assessing how creativity in HRI can be shaped or influenced by proactivity. We do so by conducting an experimental study, where the robot tries to support the user on the completion of the task regardless of the end result being novel or not and does so by exhibiting anticipatory proactive behaviors. In our study, the robot instantiates a set of verbal communications as proactive robot behavior. To our knowledge, the study is among the first to establish and investigate the relationship between creativity and proactivity in the HRI context, based on user studies. The initial results have indicated a relationship between observed proactivity, creativity, and task achievement. It also provides valuable pointers for further investigation in this domain.

Do I still like myself? Human-robot collaboration entails emotional consequences

Investigating emotional processes has been vital for understanding human-human interaction. Specifically, emotional concepts of oneself and interaction partners shape interaction style and are associated with mental health and cognitive performance. Whether these concepts are equally relevant in human-robot interaction (HRI) has not been investigated. Here, we measured emotional concepts before and after collaboration with a telepresent robot described as (a) able, (b) unable to experience emotions, or (c) autonomous without reference to emotions, compared to a (d) control condition without human-robot collaboration. Emotional concepts were measured with the affective His-Mine-Paradigm (aHMP) in which participants were asked to affectively evaluate pronoun-noun-pairs related to themselves (e.g., “my victory”) or the robot (e.g., “its victory”). Results indicated that (1) the aHMP can be validly used in HRI contexts, (2) emotional self-concept got less positive after interacting with “emotionless” robots, and emotional robot-concept got more positive after interacting with (3) “autonomous” or (4) “emotional” robots. Results suggest that beliefs about and interactions with telepresent robots can change emotional concepts which themselves are associated with well-being, performance, and interaction style. In sum, we report emotional consequences of HRI and argue that such consequences should receive more attention in future research and HRI design.

Robot Gaze Behavior Affects Honesty in Human-Robot Interaction.

As the use of humanoid robots proliferates, an increasing amount of people may find themselves face-to-“face” with a robot in everyday life. Although there is a plethora of information available on facial social cues and how we interpret them in the field of human-human social interaction, we cannot assume that these findings flawlessly transfer to human-robot interaction. Therefore, more research on facial cues in human-robot interaction is required. This study investigated deception in human-robot interaction context, focusing on the effect that eye contact with a robot has on honesty toward this robot. In an iterative task, participants could assist a humanoid robot by providing it with correct information, or potentially secure a reward for themselves by providing it with incorrect information. Results show that participants are increasingly honest after the robot establishes eye contact with them, but only if this is in response to deceptive behavior. Behavior is not influenced by the establishment of eye contact if the participant is actively engaging in honest behavior. These findings support the notion that humanoid robots can be perceived as, and treated like, social agents, since the herein described effect mirrors one present in human-human social interaction.

Emotion Recognition for Human-Robot Interaction: Recent Advances and Future Perspectives.

A fascinating challenge in the field of human–robot interaction is the possibility to endow robots with emotional intelligence in order to make the interaction more intuitive, genuine, and natural. To achieve this, a critical point is the capability of the robot to infer and interpret human emotions. Emotion recognition has been widely explored in the broader fields of human–machine interaction and affective computing. Here, we report recent advances in emotion recognition, with particular regard to the human–robot interaction context. Our aim is to review the state of the art of currently adopted emotional models, interaction modalities, and classification strategies and offer our point of view on future developments and critical issues. We focus on facial expressions, body poses and kinematics, voice, brain activity, and peripheral physiological responses, also providing a list of available datasets containing data from these modalities.

On Designing Expressive Robot Behavior: The Effect of Affective Cues on Interaction

Creating a convincing affective robot behavior is a challenging task. In this paper, we are trying to coordinate between different modalities of communication: speech, facial expressions, and gestures to make the robot interact with human users in an expressive manner. The proposed system employs videos to induce target emotions in the participants so as to start interactive discussions between each participant and the robot around the content of each video. During each experiment of interaction, the expressive ALICE robot generates an adapted multimodal behavior to the affective content of the video, and the participant evaluates its characteristics at the end of the experiment. This study discusses the multimodality of the robot behavior and its positive effect on the clarity of the emotional content of interaction. Moreover, it provides personality and gender-based evaluations of the emotional expressivity of the generated behavior so as to investigate the way it was perceived by the introverted–extroverted and male–female participants within a human–robot interaction context.

Robot Reciprocation of Hugs Increases Both Interacting Times and Self-disclosures

Physical contact like touching and hugging plays an essential role in social bonding between people by encouraging interactions and self-disclosure. However, in a human–robot interaction context, it remains unknown whether physical activity with robots provides such similar effects, even though several positive effects of touch interactions have been unveiled. Therefore, we used a hugging robot that we previously developed and experimentally investigated its physical interactions related to encouraging interactions and self-disclosure with 48 participants. Our results showed that reciprocated hugs increased the interaction times and encouraged more self-disclosure from the hugged participants than those who did not get reciprocated hugs.

Child-Robot Collaborative Problem-Solving and the Importance of Child's Voluntary Interaction: A Developmental Perspective.

The emergence and development of cognitive strategies for the transition from exploratory actions towards intentional problem-solving in children is a key question for the understanding of the development of human cognition. Researchers in developmental psychology have studied cognitive strategies and have highlighted the catalytic role of the social environment. However, it is not yet adequately understood how this capacity emerges and develops in biological systems when they perform a problem-solving task in collaboration with a robotic social agent. This paper presents an empirical study in a human-robot interaction (HRI) setting which investigates children's problem-solving from a developmental perspective. In order to theoretically conceptualize children's developmental process of problem-solving in HRI context, we use principles based on the intuitive theory and we take into consideration existing research on executive functions with a focus on inhibitory control. We considered the paradigm of the Tower of Hanoi and we conducted an HRI behavioral experiment to evaluate task performance. We designed two types of robot interventions, “voluntary” and “turn-taking”—manipulating exclusively the timing of the intervention. Our results indicate that the children who participated in the voluntary interaction setting showed a better performance in the problem solving activity during the evaluation session despite their large variability in the frequency of self-initiated interactions with the robot. Additionally, we present a detailed description of the problem-solving trajectory for a representative single case-study, which reveals specific developmental patterns in the context of the specific task. Implications and future work are discussed regarding the development of intelligent robotic systems that allow child-initiated interaction as well as targeted and not constant robot interventions.

Factors affecting trust in high-vulnerability human-robot interaction contexts: A structural equation modelling approach

The current research proposed and tested a structural equation model (SEM) that describes hypothesized relationships among factors affecting trust in human-robot interaction (HRI) such as trustworthiness, human-likeness, intelligence, perfect automation schema (PAS), and affect. A video stimulus depicting an autonomous guard robot interacting with humans was employed as a stimulus via Amazon's Mechanical Turk to recruit 233 participants. Human-related and robot-related metrics were found to affect trustworthiness that subsequently affected trust. In particular, ability (as a trustworthiness facet) was a dominant factor affecting trust in HRI. Integrity was found to mediate the relationships between robot- and human-related metrics and trustworthiness. This study also showed a correlation between intelligence and trustworthiness, as well as between PAS and trustworthiness. The findings of the present study have significant implications for both theory and practice on factors and levels that affect trust in HRI.

Design and Evaluation of a Unique Social Perception System for Human–Robot Interaction

Robot’s perception is essential for performing high-level tasks such as understanding, learning, and in general, human–robot interaction (HRI). For this reason, different perception systems have been proposed for different robotic platforms in order to detect high-level features such as facial expressions and body gestures. However, due to the variety of robotics software architectures and hardware platforms, these highly customized solutions are hardly interchangeable and adaptable to different HRI contexts. In addition, most of the developed systems have one issue in common: they detect features without awareness of the real-world contexts (e.g., detection of environmental sound assuming that it belongs to a person who is speaking, or treating a face printed on a sheet of paper as belonging to a real subject). This paper presents a novel social perception system (SPS) that has been designed to address the previous issues. SPS is an out-of-the-box system that can be integrated into different robotic platforms irrespective of hardware and software specifications. SPS detects, tracks, and delivers in real-time to robots, a wide range of human- and environment- relevant features with the awareness of their real-world contexts. We tested SPS in a typical scenario of HRI for the following purposes: to demonstrate the system capability in detecting several high-level perceptual features as well as to test the system capability to be integrated into different robotics platforms. Results show the promising capability of the system in perceiving real world in different social robotics platforms, as tested in two humanoid robots, i.e., FACE and ZENO.

Characterizing the human-robot haptic dyad in robot therapy of stroke survivors

Purpose – The working hypothesis, on which this paper is built, is that it is advantageous to look at protocols of robot rehabilitation in the general context of human-robot interaction in haptic dyads. The purpose of this paper is to propose a new method to detect and evaluate an index of active participation (AC index), underlying the performance of robot-assisted movements. This is important for avoiding the slacking phenomenon that affects robot therapy. Design/methodology/approach – The evaluation of the AC index is based on a novel technique of assistance which does not use constant or elastic forces but trains of small force impulses, with amplitude adapted to the level of impairment and a frequency of 2 Hz, which is suggested by recent results in the field of intermittent motor control. A preliminary feasibility test of the proposed method was carried out during a haptic reaching task in the absence of visual feedback, for a group of five stroke patients and an equal group of healthy subjects. Findings – The AC index appears to be stable and sensitive to training in both populations of subjects. Originality/value – The main original element of this study is the proposal of the new AC index of voluntary control associated with the new method of pulsed haptic interaction.

Toward a framework for levels of robot autonomy in human-robot interaction.

A critical construct related to human-robot interaction (HRI) is autonomy, which varies widely across robot platforms. Levels of robot autonomy (LORA), ranging from teleoperation to fully autonomous systems, influence the way in which humans and robots may interact with one another. Thus, there is a need to understand HRI by identifying variables that influence - and are influenced by - robot autonomy. Our overarching goal is to develop a framework for levels of robot autonomy in HRI. To reach this goal, the framework draws links between HRI and human-automation interaction, a field with a long history of studying and understanding human-related variables. The construct of autonomy is reviewed and redefined within the context of HRI. Additionally, the framework proposes a process for determining a robot's autonomy level, by categorizing autonomy along a 10-point taxonomy. The framework is intended to be treated as guidelines to determine autonomy, categorize the LORA along a qualitative taxonomy, and consider which HRI variables (e.g., acceptance, situation awareness, reliability) may be influenced by the LORA.

Design and Impact of Hesitation Gestures during Human-Robot Resource Conflicts

In collaborative tasks, people often communicate using nonverbal gestures to coordinate actions. When two people reach for the same object at the same time, they often respond to an imminent potential collision with jerky halting hand motions that we term hesitation gestures. Successful implementation of such communicative conflict response behaviour onto robots can be useful. In a myriad of human-robot interaction contexts involving shared spaces and objects, this behaviour can provide a fast and effective means for robots to express awareness of conflict and cede right-of-way during collaborative work with users. Our previous work suggests that when a six-degree-of-freedom (6-DOF) robot traces a simplified trajectory of recorded human hesitation gestures, these robot motions are also perceived by humans as hesitation gestures. In this work, we present a characteristic motion profile derived from the recorded human hesitation motions, called the Acceleration-based Hesitation Profile (AHP). We test its efficacy to generate communicative hesitation responses by a robot in a fast-paced human-robot interaction experiment.Compared to traditional abrupt stopping behaviours, we did not find sufficient evidence that the AHP-based robot responses improve human perception of the robot or human-robot task completion time. However, results from our in situ experiment suggest that subjects can recognize AHP-based robot responses as hesitations and distinguish them to be different from abrupt stopping behaviours.

Caregiving role in human–robot interaction: A study of the mediating effects of perceived benefit and social presence

This study investigates whether assigning a caregiving role to a robot or to its human interactant has psychological effects on the quality of human–robot interaction (HRI). College students interacted with a social robot in a between-subjects experiment (N=60) with two manipulated conditions: one where the robot played the role of an ophthalmologist (with participants serving as patients) and one where participants played the role of the ophthalmologist (with the robot serving as the patient). Results suggest that being a recipient of caregiving acts leads users to form more positive perceptions of the robot than being an ostensible caregiver to the robot. Results also indicate that perceived benefit of being in a relationship with the robot mediates the effects of the caregiving role on relationship satisfaction with—and trust towards—the robot while feelings of social presence mediate the effects on humanlike-ness and intelligence of the robot. These findings demonstrate the applicability of the Computers Are Social Actors (CASA) paradigm to the context of HRI.

Evaluating the Robot Personality and Verbal Behavior of Domestic Robots Using Video-Based Studies

Robots are increasingly being used in domestic environments and should be able to interact with inexperienced users. Human–human interaction and human–computer interaction research findings are relevant, but often limited because robots are different from both humans and computers. Therefore, new human–robot interaction (HRI) research methods can inform the design of robots suitable for inexperienced users. A video-based HRI (VHRI) methodology was here used to carry out a multi-national HRI user study for the prototype domestic robot BIRON (BIelefeld RObot companioN). Previously, the VHRI methodology was used in constrained HRI situations, while in this study HRIs involved a series of events as part of a 'hometour' scenario. Thus, the present work is the first study of this methodology in extended HRI contexts with a multi-national approach. Participants watched videos of the robot interacting with a human actor and rated two robot behaviors (Extrovert and Introvert). Participants' perceptions and ratings of the robot's behaviors differed with regard to both verbal interactions and person following by the robot. The study also confirms that the VHRI methodology provides a valuable means to obtain early user feedback, even before fully working prototypes are available. This can usefully guide the future design work on robots, and associated verbal and non-verbal behaviors.

Designing Interactions for Robot Active Learners

This paper addresses some of the problems that arise when applying active learning to the context of human-robot interaction (HRI). Active learning is an attractive strategy for robot learners because it has the potential to improve the accuracy and the speed of learning, but it can cause issues from an interaction perspective. Here we present three interaction modes that enable a robot to use active learning queries. The three modes differ in when they make queries: the first makes a query every turn, the second makes a query only under certain conditions, and the third makes a query only when explicitly requested by the teacher. We conduct an experiment in which 24 human subjects teach concepts to our upper-torso humanoid robot, Simon, in each interaction mode, and we compare these modes against a baseline mode using only passive supervised learning. We report results from both a learning and an interaction perspective. The data show that the three modes using active learning are preferable to the mode using passive supervised learning both in terms of performance and human subject preference, but each mode has advantages and disadvantages. Based on our results, we lay out several guidelines that can inform the design of future robotic systems that use active learning in an HRI setting.