During the previous field study with a robot and its interaction with mall visitors, we observed a surprising event during which a leaflet-distributing robot was abused, although it was subsequently helped by one of its previous abusers. After analyzing 72.25 hours of video data, we identified 47 cases where a robot dropped a leaflet and classified them according to following three criteria: 1) interaction between the potential helper or others with the robot before it dropped the leaflet, 2) the nature of the interaction (abused or not), and 3) whether it was helped. Using the Trajectory Equifinality Model (TEM), we analyzed 19 cases where the robot was helped. We identified the following interaction process that started with individuals who paid attention to the robot, whether they had abusive or non-abusive interactions with it, whether they noticed its failure, and finally whether they helped it. The presence of others encouraged the person to focus on the robot, and the interactions with it led to helping, regardless whether the interaction was abusive. The absence of others when the robot dropped the leaflet encouraged helping. The findings of this study will motivate interaction designs for social robots that can leverage human help.
{"title":"Qualitative Research of Robot-Helping Behaviors in a Field Trial","authors":"Sachie Yamada, Takayuki Kanda, Kanako Tomita","doi":"10.1145/3640009","DOIUrl":"https://doi.org/10.1145/3640009","url":null,"abstract":"\u0000 During the previous field study with a robot and its interaction with mall visitors, we observed a surprising event during which a leaflet-distributing robot was abused, although it was subsequently helped by one of its previous abusers. After analyzing 72.25 hours of video data, we identified 47 cases where a robot dropped a leaflet and classified them according to following three criteria: 1) interaction between the potential helper or others with the robot\u0000 before\u0000 it dropped the leaflet, 2) the nature of the interaction (abused or not), and 3) whether it was helped. Using the Trajectory Equifinality Model (TEM), we analyzed 19 cases where the robot was helped. We identified the following interaction process that started with individuals who paid attention to the robot, whether they had abusive or non-abusive interactions with it, whether they noticed its failure, and finally whether they helped it. The presence of others encouraged the person to focus on the robot, and the interactions with it led to helping, regardless whether the interaction was abusive. The absence of others when the robot dropped the leaflet encouraged helping. The findings of this study will motivate interaction designs for social robots that can leverage human help.\u0000","PeriodicalId":504644,"journal":{"name":"ACM Transactions on Human-Robot Interaction","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140716754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zahra Rezaei Khavas, Monish Reddy Kotturu, S.Reza Ahmadzadeh, Paul Robinette
The increasing use of robots in social applications requires further research on human-robot trust. The research on human-robot trust needs to go beyond the conventional definition that mainly focuses on how human-robot relations are influenced by robot performance. The emerging field of social robotics considers optimizing a robot’s personality a critical factor in user perceptions of experienced human-robot interaction (HRI). Researchers have developed trust scales that account for different dimensions of trust in HRI. These trust scales consider one performance aspect (i.e., the trust in an agent’s competence to perform a given task and their proficiency in executing the task accurately) and one moral aspect (i.e., trust in an agent’s honesty in fulfilling their stated commitments or promises) for human-robot trust. The question that arises here is to what extent do these trust aspects affect human trust in a robot? The main goal of this study is to investigate whether a robot’s undesirable behavior due to the performance trust violation would affect human trust differently than another similar undesirable behavior due to a moral trust violation. We designed and implemented an online human-robot collaborative search task that allows distinguishing between performance and moral trust violations by a robot. We ran these experiments on Prolific and recruited 100 participants for this study. Our results showed that a moral trust violation by a robot affects human trust more severely than a performance trust violation with the same magnitude and consequences.
{"title":"Do Humans Trust Robots that Violate moral trust?","authors":"Zahra Rezaei Khavas, Monish Reddy Kotturu, S.Reza Ahmadzadeh, Paul Robinette","doi":"10.1145/3651992","DOIUrl":"https://doi.org/10.1145/3651992","url":null,"abstract":"The increasing use of robots in social applications requires further research on human-robot trust. The research on human-robot trust needs to go beyond the conventional definition that mainly focuses on how human-robot relations are influenced by robot performance. The emerging field of social robotics considers optimizing a robot’s personality a critical factor in user perceptions of experienced human-robot interaction (HRI). Researchers have developed trust scales that account for different dimensions of trust in HRI. These trust scales consider one performance aspect (i.e., the trust in an agent’s competence to perform a given task and their proficiency in executing the task accurately) and one moral aspect (i.e., trust in an agent’s honesty in fulfilling their stated commitments or promises) for human-robot trust. The question that arises here is to what extent do these trust aspects affect human trust in a robot? The main goal of this study is to investigate whether a robot’s undesirable behavior due to the performance trust violation would affect human trust differently than another similar undesirable behavior due to a moral trust violation. We designed and implemented an online human-robot collaborative search task that allows distinguishing between performance and moral trust violations by a robot. We ran these experiments on Prolific and recruited 100 participants for this study. Our results showed that a moral trust violation by a robot affects human trust more severely than a performance trust violation with the same magnitude and consequences.","PeriodicalId":504644,"journal":{"name":"ACM Transactions on Human-Robot Interaction","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140250027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Joe Louca, Kerstin Eder, J. Vrublevskis, Antonia Tzemanaki
Remote manipulation is a key enabler for upcoming space activities such as in-orbit servicing and manufacture (IOSM). However, due to the large distances involved, these systems encounter unavoidable signal delays which can lead to poor performance and users adopting a disjointed, ‘move-and-wait’ style of operation. We use a robot arm teleoperated with a haptic controller to test the impact of haptic feedback on delayed (up to 2.6 s: Earth-Moon communications) teleoperation performance for two example IOSM-style tasks. This user study showed that increased latency reduced performance in all of metrics recorded. In real-time teleoperation, haptic feedback showed improvements in success rate, accuracy, contact force, velocity, and trust, but, of these, only the improvements to contact forces and moving velocity were also seen at higher latencies. Accuracy and trust improvements were lost, or even reversed, at higher latencies. Results varied between the two tasks, highlighting the need for further research into the range of task types to be encountered in teleoperated space activities. This study also provides a framework by which to explore how features other than haptic feedback can impact both performance and trust in delayed teleoperation.
{"title":"Impact of Haptic Feedback in High Latency Teleoperation for Space Applications","authors":"Joe Louca, Kerstin Eder, J. Vrublevskis, Antonia Tzemanaki","doi":"10.1145/3651993","DOIUrl":"https://doi.org/10.1145/3651993","url":null,"abstract":"Remote manipulation is a key enabler for upcoming space activities such as in-orbit servicing and manufacture (IOSM). However, due to the large distances involved, these systems encounter unavoidable signal delays which can lead to poor performance and users adopting a disjointed, ‘move-and-wait’ style of operation. We use a robot arm teleoperated with a haptic controller to test the impact of haptic feedback on delayed (up to 2.6 s: Earth-Moon communications) teleoperation performance for two example IOSM-style tasks.\u0000 This user study showed that increased latency reduced performance in all of metrics recorded. In real-time teleoperation, haptic feedback showed improvements in success rate, accuracy, contact force, velocity, and trust, but, of these, only the improvements to contact forces and moving velocity were also seen at higher latencies. Accuracy and trust improvements were lost, or even reversed, at higher latencies. Results varied between the two tasks, highlighting the need for further research into the range of task types to be encountered in teleoperated space activities. This study also provides a framework by which to explore how features other than haptic feedback can impact both performance and trust in delayed teleoperation.","PeriodicalId":504644,"journal":{"name":"ACM Transactions on Human-Robot Interaction","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140255659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Communication is crucial for human-robot collaborative tasks. In this context, legibility studies movement as the means of implicit communication between robotic systems and a human observer. This concept has been explored mostly for manipulators and humanoid robots. In contrast, little information is available in the literature about legibility of multi-robot systems or swarms, where simplicity and non-anthropomorphism of robots, along with the complexity of their interactions and aggregated behavior impose different challenges that are not encountered in single-robot scenarios. This paper investigates legibility of multi-robot systems. Hence, we extend the definition of legibility, incorporating information about high-level goals in terms of the coordination objective of the group of robots, to previous results that focused solely on the legibility of spatial goals. A set of standard multi-robot algorithms corresponding to different coordination objectives are implemented and their legibility is evaluated in a user study, where participants observe the behavior of the multi-robot system in a virtual reality setup and are asked to identify the system’s spatial goal and coordination objective. The results of the study confirmed that coordination objectives are discernible by the users, hence multi-robot systems can be controlled to be legible, in terms of spatial goal and coordination objective.
{"title":"Towards the Legibility of Multi-Robot Systems","authors":"Beatrice Capelli, María Santos, Lorenzo Sabattini","doi":"10.1145/3647984","DOIUrl":"https://doi.org/10.1145/3647984","url":null,"abstract":"Communication is crucial for human-robot collaborative tasks. In this context, legibility studies movement as the means of implicit communication between robotic systems and a human observer. This concept has been explored mostly for manipulators and humanoid robots. In contrast, little information is available in the literature about legibility of multi-robot systems or swarms, where simplicity and non-anthropomorphism of robots, along with the complexity of their interactions and aggregated behavior impose different challenges that are not encountered in single-robot scenarios. This paper investigates legibility of multi-robot systems. Hence, we extend the definition of legibility, incorporating information about high-level goals in terms of the coordination objective of the group of robots, to previous results that focused solely on the legibility of spatial goals. A set of standard multi-robot algorithms corresponding to different coordination objectives are implemented and their legibility is evaluated in a user study, where participants observe the behavior of the multi-robot system in a virtual reality setup and are asked to identify the system’s spatial goal and coordination objective. The results of the study confirmed that coordination objectives are discernible by the users, hence multi-robot systems can be controlled to be legible, in terms of spatial goal and coordination objective.","PeriodicalId":504644,"journal":{"name":"ACM Transactions on Human-Robot Interaction","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139959096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
H. Erel, Marynel Vázquez, S. Sebo, Nicole Salomons, Sarah Gillet, Brian Scassellati
A wide range of studies in Human-Robot Interaction (HRI) has shown that robots can influence the social behavior of humans. This phenomenon is commonly explained by the Media Equation. Fundamental to this theory is the idea that when faced with technology (like robots), people perceive it as a social agent with thoughts and intentions similar to those of humans. This perception guides the interaction with the technology and its predicted impact. However, HRI studies have also reported examples in which the Media Equation has been violated, that is when people treat the influence of robots differently from the influence of humans. To address this gap, we propose a model of Robot Social Influence (RoSI) with two contributing factors. The first factor is a robot’s violation of a person’s expectations, whether the robot exceeds expectations or fails to meet expectations. The second factor is a person’s social belonging with the robot, whether the person belongs to the same group as the robot or a different group. These factors are primary predictors of robots’ social influence and commonly mediate the influence of other factors. We review HRI literature and show how RoSI can explain robots’ social influence in concrete HRI scenarios.
{"title":"RoSI: A Model for Predicting Robot Social Influence","authors":"H. Erel, Marynel Vázquez, S. Sebo, Nicole Salomons, Sarah Gillet, Brian Scassellati","doi":"10.1145/3641515","DOIUrl":"https://doi.org/10.1145/3641515","url":null,"abstract":"A wide range of studies in Human-Robot Interaction (HRI) has shown that robots can influence the social behavior of humans. This phenomenon is commonly explained by the Media Equation. Fundamental to this theory is the idea that when faced with technology (like robots), people perceive it as a social agent with thoughts and intentions similar to those of humans. This perception guides the interaction with the technology and its predicted impact. However, HRI studies have also reported examples in which the Media Equation has been violated, that is when people treat the influence of robots differently from the influence of humans. To address this gap, we propose a model of Robot Social Influence (RoSI) with two contributing factors. The first factor is a robot’s violation of a person’s expectations, whether the robot exceeds expectations or fails to meet expectations. The second factor is a person’s social belonging with the robot, whether the person belongs to the same group as the robot or a different group. These factors are primary predictors of robots’ social influence and commonly mediate the influence of other factors. We review HRI literature and show how RoSI can explain robots’ social influence in concrete HRI scenarios.","PeriodicalId":504644,"journal":{"name":"ACM Transactions on Human-Robot Interaction","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139788741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
H. Erel, Marynel Vázquez, S. Sebo, Nicole Salomons, Sarah Gillet, Brian Scassellati
A wide range of studies in Human-Robot Interaction (HRI) has shown that robots can influence the social behavior of humans. This phenomenon is commonly explained by the Media Equation. Fundamental to this theory is the idea that when faced with technology (like robots), people perceive it as a social agent with thoughts and intentions similar to those of humans. This perception guides the interaction with the technology and its predicted impact. However, HRI studies have also reported examples in which the Media Equation has been violated, that is when people treat the influence of robots differently from the influence of humans. To address this gap, we propose a model of Robot Social Influence (RoSI) with two contributing factors. The first factor is a robot’s violation of a person’s expectations, whether the robot exceeds expectations or fails to meet expectations. The second factor is a person’s social belonging with the robot, whether the person belongs to the same group as the robot or a different group. These factors are primary predictors of robots’ social influence and commonly mediate the influence of other factors. We review HRI literature and show how RoSI can explain robots’ social influence in concrete HRI scenarios.
{"title":"RoSI: A Model for Predicting Robot Social Influence","authors":"H. Erel, Marynel Vázquez, S. Sebo, Nicole Salomons, Sarah Gillet, Brian Scassellati","doi":"10.1145/3641515","DOIUrl":"https://doi.org/10.1145/3641515","url":null,"abstract":"A wide range of studies in Human-Robot Interaction (HRI) has shown that robots can influence the social behavior of humans. This phenomenon is commonly explained by the Media Equation. Fundamental to this theory is the idea that when faced with technology (like robots), people perceive it as a social agent with thoughts and intentions similar to those of humans. This perception guides the interaction with the technology and its predicted impact. However, HRI studies have also reported examples in which the Media Equation has been violated, that is when people treat the influence of robots differently from the influence of humans. To address this gap, we propose a model of Robot Social Influence (RoSI) with two contributing factors. The first factor is a robot’s violation of a person’s expectations, whether the robot exceeds expectations or fails to meet expectations. The second factor is a person’s social belonging with the robot, whether the person belongs to the same group as the robot or a different group. These factors are primary predictors of robots’ social influence and commonly mediate the influence of other factors. We review HRI literature and show how RoSI can explain robots’ social influence in concrete HRI scenarios.","PeriodicalId":504644,"journal":{"name":"ACM Transactions on Human-Robot Interaction","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139848804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Naoko Abe, Yue Hu, M. Benallegue, N. Yamanobe, G. Venture, Eiichi Yoshida
Anticipating a future scenario where the robot initiates its own actions and behaves voluntarily when collaborating with humans, our research focuses on human understanding and perception of unanticipated robot actions during physical human-robot interaction. While the current literature searches for key factors that make the human-robot collaboration successful, the question of how people experience the robot’s unanticipated action as cooperative or uncooperative seems to remain open. We designed a game-based experiment (N=35) where the participant played a “catch-falling-coins” game by moving a robotic arm. Our experiment introduced unanticipated robot actions in an “active session” where the robot targeted higher-valued coins without first informing the participants. Through semi-structured interviews and statistical analysis of questionnaires (Big Five Personality Test, SAM, NARS and CH33), we examined the participants’ understanding of the robot’s “intention” and their positive or negative perception of the robot as cooperative or uncooperative. Among the participants who understood that the robot’s “intention” was to catch the higher-valued coins, the majority of them reported a positive perception of the robot (cooperative or helpful) while this was not the case among those who did not understand the robot’s intention. We also observed relevant relationships between some personality traits and a person’s understanding of the robot’s intention. Qualitative analysis of the interviews allowed us to structure the process of perception change during the game into three phases: confusion, investigation, and adaptation. We believe that our research contributes to the study of human perception, and particularly to the relationship between a human’s understanding of unanticipated robot actions and their positive or negative perception of the robot.
{"title":"Human Understanding and Perception of Unanticipated Robot Action in the Context of Physical Interaction","authors":"Naoko Abe, Yue Hu, M. Benallegue, N. Yamanobe, G. Venture, Eiichi Yoshida","doi":"10.1145/3643458","DOIUrl":"https://doi.org/10.1145/3643458","url":null,"abstract":"Anticipating a future scenario where the robot initiates its own actions and behaves voluntarily when collaborating with humans, our research focuses on human understanding and perception of unanticipated robot actions during physical human-robot interaction. While the current literature searches for key factors that make the human-robot collaboration successful, the question of how people experience the robot’s unanticipated action as cooperative or uncooperative seems to remain open. We designed a game-based experiment (N=35) where the participant played a “catch-falling-coins” game by moving a robotic arm. Our experiment introduced unanticipated robot actions in an “active session” where the robot targeted higher-valued coins without first informing the participants. Through semi-structured interviews and statistical analysis of questionnaires (Big Five Personality Test, SAM, NARS and CH33), we examined the participants’ understanding of the robot’s “intention” and their positive or negative perception of the robot as cooperative or uncooperative. Among the participants who understood that the robot’s “intention” was to catch the higher-valued coins, the majority of them reported a positive perception of the robot (cooperative or helpful) while this was not the case among those who did not understand the robot’s intention. We also observed relevant relationships between some personality traits and a person’s understanding of the robot’s intention. Qualitative analysis of the interviews allowed us to structure the process of perception change during the game into three phases: confusion, investigation, and adaptation. We believe that our research contributes to the study of human perception, and particularly to the relationship between a human’s understanding of unanticipated robot actions and their positive or negative perception of the robot.","PeriodicalId":504644,"journal":{"name":"ACM Transactions on Human-Robot Interaction","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139593056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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