Pub Date : 2020-08-01DOI: 10.1109/RO-MAN47096.2020.9223460
J. Schermer, K. Hindriks
We design and evaluate a robot interviewer for collecting visitor data in a museum for marketing purposes. We take inspiration from research on face-to-face human intercept interviews. We develop a personal interviewing style that is expected to motivate participants to answer more questions and compare this with a more formal style. We also evaluate whether a greeting ritual performed by the robot increases participation and whether taking a picture with the robot is an effective incentive for visitors to participate in an interview.Our study is conducted "in the wild" and we analyse sessions with the robot and passersby in a museum. The independent variables were interviewing style and whether an incentive was offered or not. The dependent variables were participation and continuation rate, and museum ratings. Contrary to expectations, we find that the participation rate is lower when the robot provides an incentive. Although we find that a personal style is perceived as more social, it does not influence the continuation rate. Museum ratings were also not affected by style. Our style manipulation may not have been strong enough to produce these effects.Our study shows that social robots have a high potential for conducting intercept interviews. Willingness to participate in a robot interview is high, while this is one of the main challenges with intercept interviews. To improve data collection, people detection and speech recognition skills could be improved.
{"title":"Interviewing Style for a Social Robot Engaging Museum Visitors for a Marketing Research Interview","authors":"J. Schermer, K. Hindriks","doi":"10.1109/RO-MAN47096.2020.9223460","DOIUrl":"https://doi.org/10.1109/RO-MAN47096.2020.9223460","url":null,"abstract":"We design and evaluate a robot interviewer for collecting visitor data in a museum for marketing purposes. We take inspiration from research on face-to-face human intercept interviews. We develop a personal interviewing style that is expected to motivate participants to answer more questions and compare this with a more formal style. We also evaluate whether a greeting ritual performed by the robot increases participation and whether taking a picture with the robot is an effective incentive for visitors to participate in an interview.Our study is conducted \"in the wild\" and we analyse sessions with the robot and passersby in a museum. The independent variables were interviewing style and whether an incentive was offered or not. The dependent variables were participation and continuation rate, and museum ratings. Contrary to expectations, we find that the participation rate is lower when the robot provides an incentive. Although we find that a personal style is perceived as more social, it does not influence the continuation rate. Museum ratings were also not affected by style. Our style manipulation may not have been strong enough to produce these effects.Our study shows that social robots have a high potential for conducting intercept interviews. Willingness to participate in a robot interview is high, while this is one of the main challenges with intercept interviews. To improve data collection, people detection and speech recognition skills could be improved.","PeriodicalId":383722,"journal":{"name":"2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121487187","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}
Pub Date : 2020-08-01DOI: 10.1109/RO-MAN47096.2020.9223462
Min Hun Lee, D. Siewiorek, A. Smailagic, A. Bernardino, S. Badia
A robotic exercise coaching system requires the capability of automatically assessing a patient’s exercise to in-teract with a patient and generate corrective feedback. However, even if patients have various physical conditions, most prior work on robotic exercise coaching systems has utilized generic, pre-defined feedback.This paper presents an interactive approach that combines machine learning and rule-based models to automatically assess a patient’s rehabilitation exercise and tunes with patient’s data to generate personalized corrective feedback. To generate feedback when an erroneous motion occurs, our approach applies an ensemble voting method that leverages predictions from multiple frames for frame-level assessment. According to the evaluation with the dataset of three stroke rehabilitation exercises from 15 post-stroke subjects, our interactive approach with an ensemble voting method supports more accurate frame-level assessment (p < 0.01), but also can be tuned with held-out user’s unaffected motions to significantly improve the performance of assessment from 0.7447 to 0.8235 average F1-scores over all exercises (p < 0.01). This paper discusses the value of an interactive approach with an ensemble voting method for personalized interaction of a robotic exercise coaching system.
{"title":"Towards Personalized Interaction and Corrective Feedback of a Socially Assistive Robot for Post-Stroke Rehabilitation Therapy","authors":"Min Hun Lee, D. Siewiorek, A. Smailagic, A. Bernardino, S. Badia","doi":"10.1109/RO-MAN47096.2020.9223462","DOIUrl":"https://doi.org/10.1109/RO-MAN47096.2020.9223462","url":null,"abstract":"A robotic exercise coaching system requires the capability of automatically assessing a patient’s exercise to in-teract with a patient and generate corrective feedback. However, even if patients have various physical conditions, most prior work on robotic exercise coaching systems has utilized generic, pre-defined feedback.This paper presents an interactive approach that combines machine learning and rule-based models to automatically assess a patient’s rehabilitation exercise and tunes with patient’s data to generate personalized corrective feedback. To generate feedback when an erroneous motion occurs, our approach applies an ensemble voting method that leverages predictions from multiple frames for frame-level assessment. According to the evaluation with the dataset of three stroke rehabilitation exercises from 15 post-stroke subjects, our interactive approach with an ensemble voting method supports more accurate frame-level assessment (p < 0.01), but also can be tuned with held-out user’s unaffected motions to significantly improve the performance of assessment from 0.7447 to 0.8235 average F1-scores over all exercises (p < 0.01). This paper discusses the value of an interactive approach with an ensemble voting method for personalized interaction of a robotic exercise coaching system.","PeriodicalId":383722,"journal":{"name":"2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117289794","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}
Pub Date : 2020-08-01DOI: 10.1109/RO-MAN47096.2020.9223579
Dimitrios Papageorgiou, Z. Doulgeri
In many industrial applications robot’s motion has to be subjected to spatial constraints imposed by the geometry of the task, e.g. motion of the end-effector on a surface. Current learning by demonstration methods encode the motion either in the Cartesian space of the end-effector, or in the configuration space of the robot. In those cases, the spatial generalization of the motion does not guarantee that the motion will in any case respect the spatial constraints of the task, as no knowledge of those constraints is exploited. In this work, a novel approach for encoding a kinematic behavior is proposed, which takes advantage of such a knowledge and guarantees that the motion will, in any case, satisfy the spatial constraints and the motion pattern will not be distorted. The proposed approach is compared with respect to its ability for spatial generalization, to two different dynamical system based approaches implemented on the Cartesian space via experiments.
{"title":"Learning by demonstration for constrained tasks*","authors":"Dimitrios Papageorgiou, Z. Doulgeri","doi":"10.1109/RO-MAN47096.2020.9223579","DOIUrl":"https://doi.org/10.1109/RO-MAN47096.2020.9223579","url":null,"abstract":"In many industrial applications robot’s motion has to be subjected to spatial constraints imposed by the geometry of the task, e.g. motion of the end-effector on a surface. Current learning by demonstration methods encode the motion either in the Cartesian space of the end-effector, or in the configuration space of the robot. In those cases, the spatial generalization of the motion does not guarantee that the motion will in any case respect the spatial constraints of the task, as no knowledge of those constraints is exploited. In this work, a novel approach for encoding a kinematic behavior is proposed, which takes advantage of such a knowledge and guarantees that the motion will, in any case, satisfy the spatial constraints and the motion pattern will not be distorted. The proposed approach is compared with respect to its ability for spatial generalization, to two different dynamical system based approaches implemented on the Cartesian space via experiments.","PeriodicalId":383722,"journal":{"name":"2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)","volume":"39 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120852282","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}
Pub Date : 2020-08-01DOI: 10.1109/RO-MAN47096.2020.9223559
Adna Bliek, Suna Bensch, T. Hellström
In human communication, backchanneling is an important part of the natural interaction protocol. The purpose is to signify the listener’s attention, understanding, agreement, or to indicate that a speaker should go on talking. While the effects of backchanneling robots on humans have been investigated, studies of how and when humans backchannel to talking robots is poorly studied. In this paper we investigate how the robot’s behavior as a speaker affects a human listener’s backchanneling behavior. This is interesting in Human-Robot Interaction since backchanneling between humans has been shown to support more fluid interactions, and human-robot interaction would therefore benefit from mimicking this human communication feature. The results show that backchanneling increases when the robot exhibits backchannel-inviting cues such as pauses and gestures. Furthermore, clear differences between how a human backchannels to another human and to a robot are shown.
{"title":"How Can a Robot Trigger Human Backchanneling?","authors":"Adna Bliek, Suna Bensch, T. Hellström","doi":"10.1109/RO-MAN47096.2020.9223559","DOIUrl":"https://doi.org/10.1109/RO-MAN47096.2020.9223559","url":null,"abstract":"In human communication, backchanneling is an important part of the natural interaction protocol. The purpose is to signify the listener’s attention, understanding, agreement, or to indicate that a speaker should go on talking. While the effects of backchanneling robots on humans have been investigated, studies of how and when humans backchannel to talking robots is poorly studied. In this paper we investigate how the robot’s behavior as a speaker affects a human listener’s backchanneling behavior. This is interesting in Human-Robot Interaction since backchanneling between humans has been shown to support more fluid interactions, and human-robot interaction would therefore benefit from mimicking this human communication feature. The results show that backchanneling increases when the robot exhibits backchannel-inviting cues such as pauses and gestures. Furthermore, clear differences between how a human backchannels to another human and to a robot are shown.","PeriodicalId":383722,"journal":{"name":"2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131675783","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}
Pub Date : 2020-08-01DOI: 10.1109/RO-MAN47096.2020.9223561
N. Payette, Áron Székely, G. Andrighetto
Human cooperation is both powerful and puzzling. Large-scale cooperation among genetically unrelated individuals makes humans unique with respect to all other animal species. Therefore, learning how cooperation emerges and persists is a key question for social scientists. Recently, scholars have recognized the importance of social norms as solutions to major local and large-scale collective action problems, from the management of water resources to the reduction of smoking in public places to the change in fertility practices. Yet a well-founded model of the effect of social norms on human cooperation is still lacking.We present here a version of the Experience-Weighted Attraction (EWA) reinforcement learning model that integrates norm-based considerations into its utility function that we call EWA+Norms. We compare the behaviour of this hybrid model to the standard EWA when applied to a collective risk social dilemma in which groups of individuals must reach a threshold level of cooperation to avoid the risk of catastrophe. We find that standard EWA is not sufficient for generating cooperation, but that EWA+Norms is. Next step is to compare simulation results with human behaviour in large-scale experiments.
{"title":"Social norms and cooperation in a collective-risk social dilemma: comparing reinforcing learning and norm-based approaches","authors":"N. Payette, Áron Székely, G. Andrighetto","doi":"10.1109/RO-MAN47096.2020.9223561","DOIUrl":"https://doi.org/10.1109/RO-MAN47096.2020.9223561","url":null,"abstract":"Human cooperation is both powerful and puzzling. Large-scale cooperation among genetically unrelated individuals makes humans unique with respect to all other animal species. Therefore, learning how cooperation emerges and persists is a key question for social scientists. Recently, scholars have recognized the importance of social norms as solutions to major local and large-scale collective action problems, from the management of water resources to the reduction of smoking in public places to the change in fertility practices. Yet a well-founded model of the effect of social norms on human cooperation is still lacking.We present here a version of the Experience-Weighted Attraction (EWA) reinforcement learning model that integrates norm-based considerations into its utility function that we call EWA+Norms. We compare the behaviour of this hybrid model to the standard EWA when applied to a collective risk social dilemma in which groups of individuals must reach a threshold level of cooperation to avoid the risk of catastrophe. We find that standard EWA is not sufficient for generating cooperation, but that EWA+Norms is. Next step is to compare simulation results with human behaviour in large-scale experiments.","PeriodicalId":383722,"journal":{"name":"2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123546565","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}
Pub Date : 2020-08-01DOI: 10.1109/RO-MAN47096.2020.9223608
Mojgan Hashemian, Marta Couto, S. Mascarenhas, A. Paiva, P. A. Santos, R. Prada
This paper presents the results of a user study designed to investigate social robots’ persuasiveness. In the design, the robot attempts to persuade users in two different conditions comparing to a control condition. In one condition, the robot aims at persuading users by giving them a reward. In the second condition, the robot tries to persuade by punishing users. The results indicated that the robot succeeded to persuade the users to select a less-desirable choice comparing to a better one. However, no difference was found in the perception of the robot’s warmth nor discomfort, comparing the two strategies. The results suggest that social robots are capable of persuading users objectively, but further investigation is required to investigate persuasion subjectively.
{"title":"Investigating Reward/Punishment Strategies in the Persuasiveness of Social Robots*","authors":"Mojgan Hashemian, Marta Couto, S. Mascarenhas, A. Paiva, P. A. Santos, R. Prada","doi":"10.1109/RO-MAN47096.2020.9223608","DOIUrl":"https://doi.org/10.1109/RO-MAN47096.2020.9223608","url":null,"abstract":"This paper presents the results of a user study designed to investigate social robots’ persuasiveness. In the design, the robot attempts to persuade users in two different conditions comparing to a control condition. In one condition, the robot aims at persuading users by giving them a reward. In the second condition, the robot tries to persuade by punishing users. The results indicated that the robot succeeded to persuade the users to select a less-desirable choice comparing to a better one. However, no difference was found in the perception of the robot’s warmth nor discomfort, comparing the two strategies. The results suggest that social robots are capable of persuading users objectively, but further investigation is required to investigate persuasion subjectively.","PeriodicalId":383722,"journal":{"name":"2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126751899","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}
Pub Date : 2020-08-01DOI: 10.1109/RO-MAN47096.2020.9223478
Marcus M. Scheunemann, R. Cuijpers, Christoph Salge
A solid methodology to understand human perception and preferences in human-robot interaction (HRI) is crucial in designing real-world HRI. Social cognition posits that the dimensions Warmth and Competence are central and universal dimensions characterizing other humans [1]. The Robotic Social Attribute Scale (RoSAS) proposes items for those dimensions suitable for HRI and validated them in a visual observation study. In this paper we complement the validation by showing the usability of these dimensions in a behavior based, physical HRI study with a fully autonomous robot. We compare the findings with the popular Godspeed dimensions Animacy, Anthropomorphism, Likeability, Perceived Intelligence and Perceived Safety. We found that Warmth and Competence, among all RoSAS and Godspeed dimensions, are the most important predictors for human preferences between different robot behaviors. This predictive power holds even when there is no clear consensus preference or significant factor difference between conditions.
{"title":"Warmth and Competence to Predict Human Preference of Robot Behavior in Physical Human-Robot Interaction","authors":"Marcus M. Scheunemann, R. Cuijpers, Christoph Salge","doi":"10.1109/RO-MAN47096.2020.9223478","DOIUrl":"https://doi.org/10.1109/RO-MAN47096.2020.9223478","url":null,"abstract":"A solid methodology to understand human perception and preferences in human-robot interaction (HRI) is crucial in designing real-world HRI. Social cognition posits that the dimensions Warmth and Competence are central and universal dimensions characterizing other humans [1]. The Robotic Social Attribute Scale (RoSAS) proposes items for those dimensions suitable for HRI and validated them in a visual observation study. In this paper we complement the validation by showing the usability of these dimensions in a behavior based, physical HRI study with a fully autonomous robot. We compare the findings with the popular Godspeed dimensions Animacy, Anthropomorphism, Likeability, Perceived Intelligence and Perceived Safety. We found that Warmth and Competence, among all RoSAS and Godspeed dimensions, are the most important predictors for human preferences between different robot behaviors. This predictive power holds even when there is no clear consensus preference or significant factor difference between conditions.","PeriodicalId":383722,"journal":{"name":"2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122907193","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}
Pub Date : 2020-08-01DOI: 10.1109/RO-MAN47096.2020.9223432
Birgit Lugrin, Elisabeth Ströle, David Obremski, F. Schwab, Benjamin P. Lange
The present contribution investigates the effects of spoken language varieties, in particular non-standard / regional language compared to standard language (in our study: High German), in social robotics. Based on (media) psychological and sociolinguistic research, we assumed that a robot speaking in regional language (i.e., dialect and regional accent) would be considered less competent compared to the same robot speaking in standard language (H1). Contrarily, we assumed that regional language might enhance perceived social skills and likability of a robot, at least so when taking into account whether and how much the human observers making the evaluations talk in regional language themselves. More precisely, it was assumed that the more the study participants spoke in regional language, the better their ratings of the dialect-speaking robot on social skills and likeability would be (H2). We also investigated whether the robot’s gender (male vs. female voice) would have an effect on the ratings (RQ). H1 received full, H2 limited empirical support by the data, while the robot’s gender (RQ) turned out to be a mostly negligible factor. Based on our results, practical implications for robots speaking in regional language varieties are suggested.
{"title":"What if it speaks like it was from the village? Effects of a Robot speaking in Regional Language Variations on Users’ Evaluations","authors":"Birgit Lugrin, Elisabeth Ströle, David Obremski, F. Schwab, Benjamin P. Lange","doi":"10.1109/RO-MAN47096.2020.9223432","DOIUrl":"https://doi.org/10.1109/RO-MAN47096.2020.9223432","url":null,"abstract":"The present contribution investigates the effects of spoken language varieties, in particular non-standard / regional language compared to standard language (in our study: High German), in social robotics. Based on (media) psychological and sociolinguistic research, we assumed that a robot speaking in regional language (i.e., dialect and regional accent) would be considered less competent compared to the same robot speaking in standard language (H1). Contrarily, we assumed that regional language might enhance perceived social skills and likability of a robot, at least so when taking into account whether and how much the human observers making the evaluations talk in regional language themselves. More precisely, it was assumed that the more the study participants spoke in regional language, the better their ratings of the dialect-speaking robot on social skills and likeability would be (H2). We also investigated whether the robot’s gender (male vs. female voice) would have an effect on the ratings (RQ). H1 received full, H2 limited empirical support by the data, while the robot’s gender (RQ) turned out to be a mostly negligible factor. Based on our results, practical implications for robots speaking in regional language varieties are suggested.","PeriodicalId":383722,"journal":{"name":"2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121986498","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}
Pub Date : 2020-08-01DOI: 10.1109/RO-MAN47096.2020.9223516
Jinying Lin, Qilei Zhang, R. Gomez, Keisuke Nakamura, Bo He, Guangliang Li
As a branch of reinforcement learning, interactive reinforcement learning mainly studies the interaction process between humans and agents, allowing agents to learn from the intentions of human users and adapt to their preferences. In most of the current studies, human users need to intentionally provide explicit feedback via pressing keyboard buttons or mouse clicks. However, in our paper, we proposed an interactive reinforcement learning method that facilitates an agent to learn from human social signals — facial feedback via a ordinary camera and gestural feedback via a leap motion sensor. Our method provides a natural way for ordinary people to train agents how to perform a task according to their preferences. We tested our method in two reinforcement learning benchmarking domains — LoopMaze and Tetris, and compared to the state of the art — the TAMER framework. Our experimental results show that when learning from facial feedback the recognition of which is very low, the TAMER agent can get a similar performance to that of learning from keypress feedback with slightly more feedback. When learning from gestural feedback with a more accurate recognition, the TAMER agent can obtain a similar performance to that of learning from keypress feedback with much less feedback received. Moreover, our results indicate that the recognition error of facial feedback has a large effect on the agent performance in the beginning training process than in the later training stage. Finally, our results indicate that with enough recognition accuracy, human social signals can effectively improve the learning efficiency of agents with less human feedback.
{"title":"Human Social Feedback for Efficient Interactive Reinforcement Agent Learning","authors":"Jinying Lin, Qilei Zhang, R. Gomez, Keisuke Nakamura, Bo He, Guangliang Li","doi":"10.1109/RO-MAN47096.2020.9223516","DOIUrl":"https://doi.org/10.1109/RO-MAN47096.2020.9223516","url":null,"abstract":"As a branch of reinforcement learning, interactive reinforcement learning mainly studies the interaction process between humans and agents, allowing agents to learn from the intentions of human users and adapt to their preferences. In most of the current studies, human users need to intentionally provide explicit feedback via pressing keyboard buttons or mouse clicks. However, in our paper, we proposed an interactive reinforcement learning method that facilitates an agent to learn from human social signals — facial feedback via a ordinary camera and gestural feedback via a leap motion sensor. Our method provides a natural way for ordinary people to train agents how to perform a task according to their preferences. We tested our method in two reinforcement learning benchmarking domains — LoopMaze and Tetris, and compared to the state of the art — the TAMER framework. Our experimental results show that when learning from facial feedback the recognition of which is very low, the TAMER agent can get a similar performance to that of learning from keypress feedback with slightly more feedback. When learning from gestural feedback with a more accurate recognition, the TAMER agent can obtain a similar performance to that of learning from keypress feedback with much less feedback received. Moreover, our results indicate that the recognition error of facial feedback has a large effect on the agent performance in the beginning training process than in the later training stage. Finally, our results indicate that with enough recognition accuracy, human social signals can effectively improve the learning efficiency of agents with less human feedback.","PeriodicalId":383722,"journal":{"name":"2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116933069","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}
Pub Date : 2020-08-01DOI: 10.1109/RO-MAN47096.2020.9223490
Panagiota Christodoulou, Alecia Adelaide May Reid, Dimitrios Pnevmatikos, Carlos Rioja del Rio, Nikolaos Fachantidis
Scholars have highlighted the importance of the humanoid appearance and the integration of various social cues for the design of Socially Assistive Robots for Education (SAR). However, designing a SAR for education omitting the stakeholders that will exploit it might prove a risky task. The aim of the current study, on the one hand, was to present the design of a SAR for Science Technology Engineering and Mathematics (STEM) education developed through stakeholders’ involvement in various steps of the approach. On the other hand, the study aimed to present the evaluation of the prototype robot through a STEM-oriented robot-assisted collaborative online teaching-learning sequence. Preliminary results indicate that participants endorsed the appearance and non-verbal behavior of the robot above chance level, while gender and age-related differences were revealed regarding the most appealing feature of the robot. Implications for Human-Robot Interaction are discussed.
{"title":"Students participate and evaluate the design and development of a social robot*","authors":"Panagiota Christodoulou, Alecia Adelaide May Reid, Dimitrios Pnevmatikos, Carlos Rioja del Rio, Nikolaos Fachantidis","doi":"10.1109/RO-MAN47096.2020.9223490","DOIUrl":"https://doi.org/10.1109/RO-MAN47096.2020.9223490","url":null,"abstract":"Scholars have highlighted the importance of the humanoid appearance and the integration of various social cues for the design of Socially Assistive Robots for Education (SAR). However, designing a SAR for education omitting the stakeholders that will exploit it might prove a risky task. The aim of the current study, on the one hand, was to present the design of a SAR for Science Technology Engineering and Mathematics (STEM) education developed through stakeholders’ involvement in various steps of the approach. On the other hand, the study aimed to present the evaluation of the prototype robot through a STEM-oriented robot-assisted collaborative online teaching-learning sequence. Preliminary results indicate that participants endorsed the appearance and non-verbal behavior of the robot above chance level, while gender and age-related differences were revealed regarding the most appealing feature of the robot. Implications for Human-Robot Interaction are discussed.","PeriodicalId":383722,"journal":{"name":"2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)","volume":"262 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115281570","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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