How Certain Robot Attributes Influence Human-to-Robot Social and Emotional Bonds

A growing population of humans are feeling lonely and isolated and may therefore benefit from social and emotional companionship. However, other humans cannot always be available to fulfill these needs, and such in-need individuals often cannot care for pets. Therefore, we explore how robot companions may be designed to facilitate bonds with humans. Our preliminary examination of 115 participants in a quasi-experimental study suggests that humans are more likely to develop social and emotional bonds with robots when those robots are good at communicating and conveying emotions. However, robots’ anthropomorphic attributes and responsiveness to external cues were found to have no impact on bond formulation

Robot Vulnerability and the Elicitation of User Empathy

This paper describes a between-subjects Amazon Mechanical Turk study (n = 220) that investigated how a robot’s affective narrative influences its ability to elicit empathy in human observers. We first conducted a pilot study to develop and validate the robot’s affective narratives. Then, in the full study, the robot used one of three different affective narrative strategies (funny, sad, neutral) while becoming less functional at its shopping task over the course of the interaction. As the functionality of the robot degraded, participants were repeatedly asked if they were willing to help the robot. The results showed that conveying a sad narrative significantly influenced the participants’ willingness to help the robot throughout the interaction and determined whether participants felt empathetic toward the robot throughout the interaction. Furthermore, a higher amount of past experience with robots also increased the participants’ willingness to help the robot. This work suggests that affective narratives can be useful in short-term interactions that benefit from emotional connections between humans and robot

Robot Vulnerability and the Elicitation of User Empathy

This paper describes a between-subjects Amazon Mechanical Turk study (n = 220) that investigated how a robot's affective narrative influences its ability to elicit empathy in human observers. We first conducted a pilot study to develop and validate the robot's affective narratives. Then, in the full study, the robot used one of three different affective narrative strategies (funny, sad, neutral) while becoming less functional at its shopping task over the course of the interaction. As the functionality of the robot degraded, participants were repeatedly asked if they were willing to help the robot. The results showed that conveying a sad narrative significantly influenced the participants' willingness to help the robot throughout the interaction and determined whether participants felt empathetic toward the robot throughout the interaction. Furthermore, a higher amount of past experience with robots also increased the participants' willingness to help the robot. This work suggests that affective narratives can be useful in short-term interactions that benefit from emotional connections between humans and robots.Comment: Published by and copyright protected by IEEE, 8 pages, 4 figures, 31st IEEE International Conference on Robot & Human Interactive Communication (RO-MAN 2022

Robotic Psychology. What Do We Know about Human-Robot Interaction and What Do We Still Need to Learn?

“Robotization”, the integration of robots in human life will change human life drastically. In many situations, such as in the service sector, robots will become an integrative part of our lives. Thus, it is vital to learn from extant research on human-robot interaction (HRI). This article introduces robotic psychology that aims to bridge the gap between humans and robots by providing insights into particularities of HRI. It presents a conceptualization of robotic psychology and provides an overview of research on service-focused human-robot interaction. Theoretical concepts, relevant to understand HRI with are reviewed. Major achievements, shortcomings, and propositions for future research will be discussed

Trust Violations in Human-Human and Human-Robot Interactions: The Influence of Ability, Benevolence and Integrity Violations

The present work investigated the effects of trust violations on perceptions and risk-taking behaviors, and how those effects differ in human-human versus human-machine collaborations. Participants were paired with either a human or machine teammate in a derivation of a well-known trust game. Therein, the teammate committed one of three qualitatively different trust violations (i.e., an ability-, benevolence-, or integrity-based violation of trust). The results showed that ability-based trust violations had the largest impact on perceptions of ability; the other trust violations did not have differential impacts on self-reported ability, benevolence, or integrity, or risk-taking behaviors, and none of these effects were qualified by being partnered with a human versus a robot. Additionally, humans engaged in more risk-taking behaviors when paired with a robotic partner compared to a human over time

Towards more humane machines: creating emotional social robots

Robots are now widely used in industrial settings, and today the world has woken up to the impact that they will have in our society. But robots have been limited to repetitive, industrial tasks. However, recent platforms are becoming more secure to operate amongst humans, and research in Human-Robot Interaction (HRI) is preparing robots for use in schools, public services and eventually everyone’s home. If we aim for a robot flexible enough to work around humans and decide autonomously how to act in complex situations, a notion of morality is needed for their decision making. In this chapter we argue that we can achieve some level of moral decision making in social robots if they are endowed with empathy capabilities. We then discuss how to build artificial empathy in robots, giving some concrete examples of how these implementations can guide the path to creating moral social robots in the future.info:eu-repo/semantics/acceptedVersio

Human-Robot Interaction: Mapping Literature Review and Network Analysis

Organizations increasingly adopt social robots as additions to real-life workforces, which requires knowledge of how humans react to and work with robots. The longstanding research on Human-Robot Interaction (HRI) offers relevant insights, but the existing literature reviews are limited in their ability to guide theory development and practitioners in sustainably employing social robots because the reviews lack a systematic synthesis of HRI concepts, relationships, and ensuing effects. This study offers a mapping review of the past ten years of HRI research. With the analysis of 68 peer-reviewed journal articles, we identify shifting foci, for example, towards more application-specific empirical investigations, and the most prominent concepts and relationships investigated in connection with social robots, for example, robot appearance. The results offer Information Systems scholars and practitioners an initial knowledge base and nuanced insights into key predictors and outcome variables that can hinder and foster social robot adoption in the workplace