843 research outputs found
Exploring miscommunication and collaborative behaviour in human-robot interaction
This paper presents the first step in designing a speech-enabled robot that is capable of natural management of miscommunication. It describes the methods
and results of two WOz studies, in which
dyads of naĂŻve participants interacted in a
collaborative task. The first WOz study
explored human miscommunication
management. The second study investigated
how shared visual space and monitoring
shape the processes of feedback and communication in task-oriented interactions.
The results provide insights for the development of human-inspired and
robust natural language interfaces in robots
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A corpus-based analysis of route instructions in human-robot interaction
This paper investigates how users employ spatial descriptions to navigate a speech-enabled robot. We created a simulated environment in which users gave route instructions in a dialogic real-time interaction with a robot, which was
operated by naĂŻve participants. The ability of robot monitoring was also manipulated in two experimental conditions. The results provide evidence that the content of the instructions and strategies of the users vary depending on the conditions and
demands of the interaction. As expected, the route instructions frequently were underspecified and arbitrary. The findings of
this study elucidate the complexity in interpreting spatial language in HRI. However, they also point to the need for
endowing mobile robots with richer dialogue resources to compensate for the uncertainties arising from language as well
as the environment
How Do You Like Me in This: User Embodiment Preferences for Companion Agents
We investigate the relationship between the embodiment of an artificial companion and user perception and interaction with it. In a Wizard of Oz study, 42 users interacted with one of two embodiments: a physical robot or a virtual agent on a screen through a role-play of secretarial tasks in an office, with the companion providing essential assistance. Findings showed that participants in both condition groups when given the choice would prefer to interact with the robot companion, mainly for its greater physical or social presence. Subjects also found the robot less annoying and talked to it more naturally. However, this preference for the robotic embodiment is not reflected in the usersâ actual rating of the companion or their interaction with it. We reflect on this contradiction and conclude that in a task-based context a user focuses much more on a companionâs behaviour than its embodiment. This underlines the feasibility of our efforts in creating companions that migrate between embodiments while maintaining a consistent identity from the userâs point of view
How language of interaction affects the user perception of a robot
Spoken language is the most natural way for a human to communicate with a
robot. It may seem intuitive that a robot should communicate with users in
their native language. However, it is not clear if a user's perception of a
robot is affected by the language of interaction.
We investigated this question by conducting a study with twenty-three native
Czech participants who were also fluent in English. The participants were
tasked with instructing the Pepper robot on where to place objects on a shelf.
The robot was controlled remotely using the Wizard-of-Oz technique. We
collected data through questionnaires, video recordings, and a post-experiment
feedback session. The results of our experiment show that people perceive an
English-speaking robot as more intelligent than a Czech-speaking robot (z =
18.00, p-value = 0.02). This finding highlights the influence of language on
human-robot interaction. Furthermore, we discuss the feedback obtained from the
participants via the post-experiment sessions and its implications for HRI
design.Comment: ICSR 202
Researching interactions between humans and machines: methodological challenges
Communication scholars are increasingly concerned with interactions between humans and communicative agents. These agents, however, are considerably different from digital or social media: They are designed and perceived as life-like communication partners (i.e., as âcommunicative subjectsâ), which in turn poses distinct challenges for their empirical study. Hence, in this paper, we document, discuss, and evaluate potentials and pitfalls that typically arise for communication scholars when investigating simulated or non-simulated interactions between humans and chatbots, voice assistants, or social robots. In this paper, we focus on experiments (including pre-recorded stimuli, vignettes and the âWizard of Ozâ-technique) and field studies. Overall, this paper aims to provide guidance and support for communication scholars who want to empirically study human-machine communication. To this end, we not only compile potential challenges, but also recommend specific strategies and approaches. In addition, our reflections on current methodological challenges serve as a starting point for discussions in communication science on how meaning-making between humans and machines can be investigated in the best way possible, as illustrated in the concluding section
Conversational Interfaces for Explainable AI: A Human-Centered Approach
One major goal of Explainable Artificial Intelligence (XAI), in order to enhance trust in technology, is to enable the user to enquire information and explanation about its functionality directly from an intelligent agent. We propose conversational interfaces (CI) to be the perfect setting, since they are intuitive for humans and computationally processible. While there are many approaches addressing technical issues of this human-agent communication problem, the user perspective appears to be widely neglected. With the purpose of better requirement understanding and identification of implicit expectations from a human-centered view, a Wizard of Oz experiment was conducted, where participants tried to elicit basic information from a pretended artificial agent (What are your capabilities?). The hypothesis that users pursue fundamentally different strategies could be verified with the help of Conversation Analysis. Results illustrate the vast variety in human communication and disclose both requirements of users and obstacles in the implementation of protocols for interacting agents. Finally, we infer essential indications for the implementation of such a CI
Is a humorous robot more trustworthy?
As more and more social robots are being used for collaborative activities
with humans, it is crucial to investigate mechanisms to facilitate trust in the
human-robot interaction. One such mechanism is humour: it has been shown to
increase creativity and productivity in human-human interaction, which has an
indirect influence on trust. In this study, we investigate if humour can
increase trust in human-robot interaction. We conducted a between-subjects
experiment with 40 participants to see if the participants are more likely to
accept the robot's suggestion in the Three-card Monte game, as a trust check
task. Though we were unable to find a significant effect of humour, we discuss
the effect of possible confounding variables, and also report some interesting
qualitative observations from our study: for instance, the participants
interacted effectively with the robot as a team member, regardless of the
humour or no-humour condition.Comment: ICSR 202
Comparing Robot and Human guided Personalization: Adaptive Exercise Robots are Perceived as more Competent and Trustworthy
Schneider S, Kummert F. Comparing Robot and Human guided Personalization: Adaptive Exercise Robots are Perceived as more Competent and Trustworthy. INTERNATIONAL JOURNAL OF SOCIAL ROBOTICS. 2020.Learning and matching a user's preference is an essential aspect of achieving a productive collaboration in long-term Human-Robot Interaction (HRI). However, there are different techniques on how to match the behavior of a robot to a user's preference. The robot can be adaptable so that a user can change the robot's behavior to one's need, or the robot can be adaptive and autonomously tries to match its behavior to the user's preference. Both types might decrease the gap between a user's preference and the actual system behavior. However, the Level of Automation (LoA) of the robot is different between both methods. Either the user controls the interaction, or the robot is in control. We present a study on the effects of different LoAs of a Socially Assistive Robot (SAR) on a user's evaluation of the system in an exercising scenario. We implemented an online preference learning system and a user-adaptable system. We conducted a between-subject design study (adaptable robot vs. adaptive robot) with 40 subjects and report our quantitative and qualitative results. The results show that users evaluate the adaptive robots as more competent, warm, and report a higher alliance. Moreover, this increased alliance is significantly mediated by the perceived competence of the system. This result provides empirical evidence for the relation between the LoA of a system, the user's perceived competence of the system, and the perceived alliance with it. Additionally, we provide evidence for a proof-of-concept that the chosen preference learning method (i.e., Double Thompson Sampling (DTS)) is suitable for online HRI
A field study on Polish customers' attitude towards a service robot in a cafe
More and more stores in Poland are adopting robots as customer assistants or
promotional tools. However, customer attitudes to such novelty remain
unexplored. This study focused on the role of social robots in self-service
cafes. This domain has not been explored in Poland before, and there is not
much research in other countries as well. We conducted a field study in two
cafes with a teleoperated robot Nao, which sat next to the counter serving as
an assistant to a human barista. We observed customer behavior, conducted
semi-structured interviews and questionnaires with the customers. The results
show that Polish customers are neutral and insecure about robots. However, they
do not exhibit a total dislike of these technologies. We considered three
stages of the interaction and identified features of each stage that need to be
designed carefully to yield user satisfaction.Comment: 14 pages, 1 figur
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