Pub Date : 2023-11-13DOI: 10.1080/01691864.2023.2279600
Sumitaka Honji, Hikaru Arita, Kenji Tahara
AbstractSoft robots have high adaptability and safety due to their softness and are therefore widely used in human society. However, the controllability of soft robots to perform dexterous behaviors is insufficient when considering soft robots as alternative laborers for humans. Model-based control methods are effective for achieving dexterous behaviors. To build a suitable control model, problems based on specific properties, such as creep behavior and variable motions, must be addressed. In this paper, a lumped parameterized model for soft fingers with viscoelastic joints is established to address creep behavior. The parameters are expressed as distributions, which allows the model to account for motion variability. Furthermore, stochastic analyzes are performed based on the parameter distributions. The model results are consistent with the experimental results, and the model enables the investigation of the effects of various parameters related to robot variability.Keywords: Lumped parameterized modeldistributed viscoelastic parameterrandom variable transformationsensitivity analysis AcknowledgmentsWe greatly appreciate the funding sources. Additionally, we would like to thank the members of the HCR lab for their useful discussions.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the Grant-in-Aid for Scientific Research (A) No. 20H00610 of the Japan Society for the Promotion of Science (JSPS).Notes on contributorsSumitaka HonjiSumitaka Honji received the B.S. and M.S. degrees from the Department of Mechanical Engineering in the School of Engineering, Kyushu University, Japan, in 2019 and 2021, respectively. He is now a doctoral student at Kyushu University. His interests include the modeling and control of soft robotic systems.Hikaru AritaHikaru Arita received his B.S., M.S., and Ph.D. in engineering from the University of Electro-Communications, Japan, in 2012, 2014, and 2019. He served several institutions, including OMRON Corporation, Kyoto, Japan, where he worked from 2014 to 2016, and Ritsumeikan University, where he was an Assistant Professor from 2019 to 2022. He is currently an Assistant Professor at the Department of Mechanical Engineering, Faculty of Engineering, Kyushu University, Japan. His current research interests include proximity sensors, sensor-based control, musculoskeletal robots, robot hands, manipulation, and soft robots.Kenji TaharaKenji Tahara received a B.S. degree in Mech. Eng. in 1998, an M.S. degree in Info. Sci. and Syst. in 2000, and a Ph.D. degree in Robotics in 2003, all from Ritsumeikan University, Japan. From 2003 to 2007, he joined the Bio-mimetic Control Research Center of RIKEN as a Research Scientist. In 2007, he joined Kyushu University as a tenure-track Associate Professor, and in 2011, he was an Associate Professor at the Department of Mechanical Engineering, Faculty of Engineering, Kyushu University, Japan. Since
摘要软体机器人因其柔软性而具有较高的适应性和安全性，因此在人类社会中得到了广泛的应用。然而，考虑到软机器人作为人类的替代劳动力，其灵巧行为的可控性是不够的。基于模型的控制方法是实现灵巧行为的有效方法。为了建立一个合适的控制模型，必须解决基于特定属性的问题，例如蠕变行为和可变运动。本文建立了具有粘弹性关节的软手指的集总参数化模型。参数以分布形式表示，这使得模型能够考虑到运动的可变性。在此基础上，对参数分布进行了随机分析。模型结果与实验结果一致，该模型能够研究与机器人可变性相关的各种参数的影响。关键词:集总参数化模型分布粘弹性参数随机变量变换敏感性分析致谢感谢资金来源。此外，我们要感谢人权专员办事处实验室成员进行了有益的讨论。披露声明作者未报告潜在的利益冲突。本研究由日本科学促进会(JSPS)科学研究资助基金(A) No. 20H00610资助。sumitaka Honji于2019年和2021年分别获得日本九州大学工程学院机械工程系学士和硕士学位。他现在是九州大学的博士生。他的兴趣包括软机器人系统的建模和控制。Hikaru Arita，分别于2012年、2014年和2019年在日本电子通信大学(University of Electro-Communications)获得工学学士、硕士和博士学位。他曾在多家机构任职，包括2014年至2016年在日本京都的欧姆龙公司(OMRON Corporation)工作，以及2019年至2022年在立命馆大学(Ritsumeikan University)担任助理教授。他目前是日本九州大学工程学院机械工程系助理教授。他目前的研究兴趣包括接近传感器，基于传感器的控制，肌肉骨骼机器人，机械手，操作和软机器人。田原健二获得机械学士学位。Eng。1998年获得信息学硕士学位。科学。和系统。2000年获得机器人博士学位，2003年获得机器人博士学位，均毕业于日本立命馆大学。2003年至2007年，他加入RIKEN仿生控制研究中心，担任研究科学家。2007年加入九州大学任终身副教授，2011年任日本九州大学工学院机械工程系副教授。自2020年起任该系正教授。他目前的研究方向包括多指机械手的力学、设计和控制、软机器人技术(包括聚合人工肌肉驱动器)、力控制、两足机器人、人体运动分析和实现。
{"title":"Stochastic approach for modeling soft fingers with creep behavior","authors":"Sumitaka Honji, Hikaru Arita, Kenji Tahara","doi":"10.1080/01691864.2023.2279600","DOIUrl":"https://doi.org/10.1080/01691864.2023.2279600","url":null,"abstract":"AbstractSoft robots have high adaptability and safety due to their softness and are therefore widely used in human society. However, the controllability of soft robots to perform dexterous behaviors is insufficient when considering soft robots as alternative laborers for humans. Model-based control methods are effective for achieving dexterous behaviors. To build a suitable control model, problems based on specific properties, such as creep behavior and variable motions, must be addressed. In this paper, a lumped parameterized model for soft fingers with viscoelastic joints is established to address creep behavior. The parameters are expressed as distributions, which allows the model to account for motion variability. Furthermore, stochastic analyzes are performed based on the parameter distributions. The model results are consistent with the experimental results, and the model enables the investigation of the effects of various parameters related to robot variability.Keywords: Lumped parameterized modeldistributed viscoelastic parameterrandom variable transformationsensitivity analysis AcknowledgmentsWe greatly appreciate the funding sources. Additionally, we would like to thank the members of the HCR lab for their useful discussions.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the Grant-in-Aid for Scientific Research (A) No. 20H00610 of the Japan Society for the Promotion of Science (JSPS).Notes on contributorsSumitaka HonjiSumitaka Honji received the B.S. and M.S. degrees from the Department of Mechanical Engineering in the School of Engineering, Kyushu University, Japan, in 2019 and 2021, respectively. He is now a doctoral student at Kyushu University. His interests include the modeling and control of soft robotic systems.Hikaru AritaHikaru Arita received his B.S., M.S., and Ph.D. in engineering from the University of Electro-Communications, Japan, in 2012, 2014, and 2019. He served several institutions, including OMRON Corporation, Kyoto, Japan, where he worked from 2014 to 2016, and Ritsumeikan University, where he was an Assistant Professor from 2019 to 2022. He is currently an Assistant Professor at the Department of Mechanical Engineering, Faculty of Engineering, Kyushu University, Japan. His current research interests include proximity sensors, sensor-based control, musculoskeletal robots, robot hands, manipulation, and soft robots.Kenji TaharaKenji Tahara received a B.S. degree in Mech. Eng. in 1998, an M.S. degree in Info. Sci. and Syst. in 2000, and a Ph.D. degree in Robotics in 2003, all from Ritsumeikan University, Japan. From 2003 to 2007, he joined the Bio-mimetic Control Research Center of RIKEN as a Research Scientist. In 2007, he joined Kyushu University as a tenure-track Associate Professor, and in 2011, he was an Associate Professor at the Department of Mechanical Engineering, Faculty of Engineering, Kyushu University, Japan. Since","PeriodicalId":7261,"journal":{"name":"Advanced Robotics","volume":"30 14","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136281679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-13DOI: 10.1080/01691864.2023.2279610
Kazuya Tsubokura, Yurie Iribe, Norihide Kitaoka
AbstractAlthough automated dialog systems are now being used in various applications, it is difficult to say whether they will ever be able to acquire the ability to converse as naturally as people do. As a result, various methods for detecting dialog breakdowns have been proposed. However, the effect of the user's personality on breakdown detection accuracy and user response to these breakdowns have not been sufficiently examined. Therefore, in this study we analyze the relationship between user personality traits and individual differences in responses to dialog breakdowns by conducting dialog experiments.Keywords: Dialog systemdialog breakdownpersonality traits AcknowledgmentThis work was supported by JSPS KAKENHI Grant Numbers JP22K19793, JP23H00493.Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 https://taku910.github.io/mecab/.2 When we first calculated the correlations between the part of speech features and the overall personality trait scores, no strong correlations were observed, so we then used the personality trait scores of the upper and lower groups for each personality trait when performing the U-tests, in order to reveal possible relationships.Additional informationNotes on contributorsKazuya TsubokuraKazuya Tsubokura recieved his B.S. and M.S. degrees in Information Science and Technology from Aichi Prefectural University in 2021 and 2023, respectively. He is currently a Ph.D. student in Aichi Prefectural University. His research interests include spoken dialogue systems.Yurie IribeYurie Iribe received the B.E. degree in Systems Engineering from Nagoya Institute of Technology and M.S. degree in Human Informatics from Nagoya University in 1999 and 2001. She became a research associate in the Information and Media Center at Toyohashi University of Technology in 2004. She received her Ph.D. degree from Nagoya University in 2007. She is currently an Associate Professor in Aichi Prefectural University from 2017. Her research interests include speech processing and human interface.Norihide KitaokaNorihide Kitaoka received his B.S. and M.S. degrees from Kyoto University, Japan. In 1994, he joined DENSO CORPORATION. In 2000, he received his Ph.D. degree from Toyohashi University of Technology (TUT), Japan. He joined TUT as a research associate in 2001 and was a lecturer from 2003 to 2006. He was an associate professor at Nagoya University, Japan, from 2006 to 2014 and joined Tokushima University, Japan, as a professor in 2014. He has been a professor at TUT since 2018. His research interests include speech processing, speech recognition, and spoken dialog systems. He is a member of IEEE, International Speech Communication Association (ISCA), Asia Pacific Signal and Information Processing Association (APSIPA), The Institute of Electronics, Information and Communication Engineers (IEICE), Information Processing Society of Japan (IPSJ), Acoustical Society of Japan (ASJ), The Japanese Society for Ar
{"title":"Analysis of the relationship between user response to dialog breakdown and personality traits","authors":"Kazuya Tsubokura, Yurie Iribe, Norihide Kitaoka","doi":"10.1080/01691864.2023.2279610","DOIUrl":"https://doi.org/10.1080/01691864.2023.2279610","url":null,"abstract":"AbstractAlthough automated dialog systems are now being used in various applications, it is difficult to say whether they will ever be able to acquire the ability to converse as naturally as people do. As a result, various methods for detecting dialog breakdowns have been proposed. However, the effect of the user's personality on breakdown detection accuracy and user response to these breakdowns have not been sufficiently examined. Therefore, in this study we analyze the relationship between user personality traits and individual differences in responses to dialog breakdowns by conducting dialog experiments.Keywords: Dialog systemdialog breakdownpersonality traits AcknowledgmentThis work was supported by JSPS KAKENHI Grant Numbers JP22K19793, JP23H00493.Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 https://taku910.github.io/mecab/.2 When we first calculated the correlations between the part of speech features and the overall personality trait scores, no strong correlations were observed, so we then used the personality trait scores of the upper and lower groups for each personality trait when performing the U-tests, in order to reveal possible relationships.Additional informationNotes on contributorsKazuya TsubokuraKazuya Tsubokura recieved his B.S. and M.S. degrees in Information Science and Technology from Aichi Prefectural University in 2021 and 2023, respectively. He is currently a Ph.D. student in Aichi Prefectural University. His research interests include spoken dialogue systems.Yurie IribeYurie Iribe received the B.E. degree in Systems Engineering from Nagoya Institute of Technology and M.S. degree in Human Informatics from Nagoya University in 1999 and 2001. She became a research associate in the Information and Media Center at Toyohashi University of Technology in 2004. She received her Ph.D. degree from Nagoya University in 2007. She is currently an Associate Professor in Aichi Prefectural University from 2017. Her research interests include speech processing and human interface.Norihide KitaokaNorihide Kitaoka received his B.S. and M.S. degrees from Kyoto University, Japan. In 1994, he joined DENSO CORPORATION. In 2000, he received his Ph.D. degree from Toyohashi University of Technology (TUT), Japan. He joined TUT as a research associate in 2001 and was a lecturer from 2003 to 2006. He was an associate professor at Nagoya University, Japan, from 2006 to 2014 and joined Tokushima University, Japan, as a professor in 2014. He has been a professor at TUT since 2018. His research interests include speech processing, speech recognition, and spoken dialog systems. He is a member of IEEE, International Speech Communication Association (ISCA), Asia Pacific Signal and Information Processing Association (APSIPA), The Institute of Electronics, Information and Communication Engineers (IEICE), Information Processing Society of Japan (IPSJ), Acoustical Society of Japan (ASJ), The Japanese Society for Ar","PeriodicalId":7261,"journal":{"name":"Advanced Robotics","volume":"30 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136281683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-13DOI: 10.1080/01691864.2023.2277159
Jinjae Lee, Casey C. Bennett, Cedomir Stanojevic, Seongcheol Kim, Zachary Henkel, Kenna Baugus, Jennifer A. Piatt, Cindy Bethel, Selma Sabanovic
AbstractSocially-assistive robots (SARs) have significant potential to help manage chronic diseases (e.g. dementia, depression, diabetes) in spaces where people live, averse to clinic-based care. However, the challenge is designing SARs so that they perform appropriate interactions with people who have different characteristics, such as age, gender, and cultural identity. Those characteristics impact how human behaviors are performed as well as user expectations of robot responses. Although cross-cultural studies with robots have been conducted to understand differing population characteristics, they have mainly focused on statistical comparisons of groups. In this study, we utilize deep learning (DL) and machine learning (ML) models to evaluate whether cultural differences show up in robotic sensor data during human-robot interaction (HRI). To do so, a SAR was distributed to user's homes for three weeks in the US and Korea (25 participants), while collecting data on the human activity and the surrounding environment through on-board sensor devices. DL models based on that data were able to predict the user’s cultural identity with roughly 95% accuracy. Such findings have potential implications for the design and development of culturally-adaptive SARs to provide services across diverse cultural locales and multi-cultural environments where users’ cultural background cannot be assumed a priori.KEYWORDS: Human-robot interactiondeep learningcross-cultural roboticsadaptive robot designhuman activity recognition Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by separate funding mechanisms in South Korea and the United States: KOR – Hanyang University Research Fund [grant number HY-2020]; USA – National Science Foundation [grant number IIS-1900683].Notes on contributorsJinjae LeeJinjae Lee is currently a Master’s student in Data Science at the Department of Intelligence Computing at Hanyang University. He is interested in the application of machine learning and human-robot interaction to healthcare problems.Casey C. BennettDr. Casey C. Bennett is an Associate Professor in the Department of Intelligence Computing at Hanyang University in Seoul, Korea. He specializes in artificial intelligence and robotics in healthcare, including the use of data science and machine learning to create better human-robot interaction. He completed his Ph.D. at Indiana University in the US.Cedomir StanojevicDr. Cedomir Stanojevic is an Assistant Professor in the Department of Parks, Recreation & Tourism Management at Clemson University’s College of Behavioral, Social and Health Sciences, SC, U.S.A. He specializes in recreational therapy and interventions related to leisure and improved quality of life, focusing on socially assistive robotics and ecological momentary assessment to improve various populations’ health outcomes. He completed his Ph.D. at Indiana University in the US.Seong
社会辅助机器人(sar)具有巨大的潜力，可以在人们居住的空间中帮助管理慢性病(例如痴呆症、抑郁症、糖尿病)，而不是基于临床的护理。然而，挑战在于设计SARs，使它们能够与具有不同特征(如年龄、性别和文化身份)的人进行适当的互动。这些特征影响着人类行为的表现以及用户对机器人反应的期望。虽然对机器人进行的跨文化研究是为了了解不同的人口特征，但它们主要集中在群体的统计比较上。在本研究中，我们利用深度学习(DL)和机器学习(ML)模型来评估在人机交互(HRI)期间机器人传感器数据中是否存在文化差异。为此，研究人员向美国和韩国的用户(25名参与者)分发了一个SAR，为期三周，同时通过机载传感器设备收集人类活动和周围环境的数据。基于这些数据的深度学习模型能够以大约95%的准确率预测用户的文化身份。这些发现对设计和开发具有文化适应性的sar具有潜在的意义，以便在不同的文化地点和多元文化环境中提供服务，在这些环境中，用户的文化背景不能被先验地假设。关键词:人机交互深度学习跨文化机器人自适应机器人设计人类活动识别披露声明作者未报告潜在利益冲突。本工作由韩国和美国的不同资助机制支持:KOR -汉阳大学研究基金[批准号HY-2020];美国国家科学基金会[资助号is -1900683]。本文作者sjinjae Lee目前是汉阳大学智能计算系数据科学专业的硕士生。他对机器学习和人机交互在医疗保健问题中的应用感兴趣。Casey C. bennett博士Casey C. Bennett是韩国首尔汉阳大学智能计算系的副教授。他专注于医疗保健领域的人工智能和机器人技术，包括使用数据科学和机器学习来创造更好的人机交互。他在美国印第安纳大学完成了博士学位。Cedomir StanojevicDr。Cedomir Stanojevic是美国克莱姆森大学行为、社会和健康科学学院公园、娱乐和旅游管理系的助理教授，他擅长与休闲和改善生活质量相关的娱乐治疗和干预，专注于社会辅助机器人和生态瞬间评估，以改善各种人群的健康结果。他在美国印第安纳大学完成了博士学位。Seongcheol Kim在汉阳大学智能计算系获得数据科学硕士学位。他对深度学习、自然语言处理和人机交互对医疗保健问题感兴趣。Zachary Henkel是密西西比州立大学计算机科学与工程系的一名博士生。他对人机交互和社会机器人相关工程问题感兴趣。Kenna Baugus是密西西比州立大学计算机科学与工程系的一名研究生。她对人机交互和社交机器人相关的工程相关问题感兴趣。詹妮弗·a·皮亚特博士Jennifer A. Piatt是印第安纳大学布卢明顿公共卫生学院健康与健康设计系的副教授。她的专长是与社区康复相关的娱乐治疗和干预。专注于社会辅助机器人作为一种治疗干预，sge旨在了解新兴技术如何解决临床结果。辛迪BethelDr。Cindy Bethel是密西西比州立大学计算机科学与工程系的Billie J. Ball受聘教授。她是社会，治疗和机器人系统(STaRS)实验室的主任，专注于人机交互和治疗机器人宠物。塞尔玛SabanovicDr。塞尔玛·萨巴诺维奇(Selma Sabanovic)是印第安纳大学布卢明顿分校信息学和认知科学教授，负责R-House人机交互实验室。她的工作将计算的社会研究与人机交互和社交机器人的研究结合起来。她探讨了社会互动和辅助机器人在不同社会和文化背景下的设计、使用和后果，包括不同的国家、家庭、学校和医疗环境。 她在伦斯勒理工学院完成了科学与技术研究博士学位。
{"title":"Detecting cultural identity via robotic sensor data to understand differences during human-robot interaction","authors":"Jinjae Lee, Casey C. Bennett, Cedomir Stanojevic, Seongcheol Kim, Zachary Henkel, Kenna Baugus, Jennifer A. Piatt, Cindy Bethel, Selma Sabanovic","doi":"10.1080/01691864.2023.2277159","DOIUrl":"https://doi.org/10.1080/01691864.2023.2277159","url":null,"abstract":"AbstractSocially-assistive robots (SARs) have significant potential to help manage chronic diseases (e.g. dementia, depression, diabetes) in spaces where people live, averse to clinic-based care. However, the challenge is designing SARs so that they perform appropriate interactions with people who have different characteristics, such as age, gender, and cultural identity. Those characteristics impact how human behaviors are performed as well as user expectations of robot responses. Although cross-cultural studies with robots have been conducted to understand differing population characteristics, they have mainly focused on statistical comparisons of groups. In this study, we utilize deep learning (DL) and machine learning (ML) models to evaluate whether cultural differences show up in robotic sensor data during human-robot interaction (HRI). To do so, a SAR was distributed to user's homes for three weeks in the US and Korea (25 participants), while collecting data on the human activity and the surrounding environment through on-board sensor devices. DL models based on that data were able to predict the user’s cultural identity with roughly 95% accuracy. Such findings have potential implications for the design and development of culturally-adaptive SARs to provide services across diverse cultural locales and multi-cultural environments where users’ cultural background cannot be assumed a priori.KEYWORDS: Human-robot interactiondeep learningcross-cultural roboticsadaptive robot designhuman activity recognition Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by separate funding mechanisms in South Korea and the United States: KOR – Hanyang University Research Fund [grant number HY-2020]; USA – National Science Foundation [grant number IIS-1900683].Notes on contributorsJinjae LeeJinjae Lee is currently a Master’s student in Data Science at the Department of Intelligence Computing at Hanyang University. He is interested in the application of machine learning and human-robot interaction to healthcare problems.Casey C. BennettDr. Casey C. Bennett is an Associate Professor in the Department of Intelligence Computing at Hanyang University in Seoul, Korea. He specializes in artificial intelligence and robotics in healthcare, including the use of data science and machine learning to create better human-robot interaction. He completed his Ph.D. at Indiana University in the US.Cedomir StanojevicDr. Cedomir Stanojevic is an Assistant Professor in the Department of Parks, Recreation & Tourism Management at Clemson University’s College of Behavioral, Social and Health Sciences, SC, U.S.A. He specializes in recreational therapy and interventions related to leisure and improved quality of life, focusing on socially assistive robotics and ecological momentary assessment to improve various populations’ health outcomes. He completed his Ph.D. at Indiana University in the US.Seong","PeriodicalId":7261,"journal":{"name":"Advanced Robotics","volume":"30 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136281680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Special Issue on Multimodal processing and robotics for dialogue systems (Part 1)","authors":"David Traum, Gabriel Skantze, Hiromitsu Nishizaki, Ryuichiro Higashinaka, Takashi Minato, Takayuki Nagai","doi":"10.1080/01691864.2023.2276549","DOIUrl":"https://doi.org/10.1080/01691864.2023.2276549","url":null,"abstract":"\"Special Issue on Multimodal processing and robotics for dialogue systems (Part 1).\" Advanced Robotics, 37(21), pp. 1347–1348","PeriodicalId":7261,"journal":{"name":"Advanced Robotics","volume":"11 17","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135972808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-26DOI: 10.1080/01691864.2023.2270792
Dachang Zhu, Puchen Zhu, Yonglong He
AbstractTo solve the influence of uncertainties such as unmodeled errors and external disturbances on the trajectory tracking accuracy of the end-effector of a robot manipulator, a novel fuzzy super-twisting second-order sliding mode control method is proposed in this paper. Based on the dynamic model of the robot manipulator, a second-order sliding mode control algorithm is proposed by using the super-twisting to determine the non-singular terminal sliding manifold. An adaptive fuzzy algorithm is presented to compensate for the super-twisting second-order sliding mode control system for handling the chattering and overestimating the controller gains. The stability of the proposed controller is verified by the Lyapunov stability theory. Simulation and experimental results show that the proposed control method can enable the robot to track the trajectory accurately under complex and uncertain conditions and effectively suppress the chattering phenomenon of the system.Keywords: Robot manipulatortrajectory trackingadaptive fuzzy super-twisting algorithmsecond-order sliding mode control Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThe author would like to express his gratitude for the support of the University Scientific Research Project of Education Bureau of Guangzhou Municipality under Grant 202032821 and the Guangzhou City School Joint Project under Grant SL2023A03J00681.Notes on contributorsDachang ZhuDachang Zhu received the B.S. degree in mechanics design and the M.S. degree in theoretical mechanics from the Jiangxi University of Technology and Science, Ganzhou, China, in 1996 and 1999, respectively, the Ph.D. degree in mechanical engineering from Beijing Jiaotong University, Beijing, China, in 2008, and the Postdoctor in mechanical engineering from the South China University of Technology, Guangzhou, China, in 2012. He currently works as full professor with the School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou, China. His research interests include topology optimization theory and applications in compliant mechanisms, robotics, and feedback control of the dynamic systems.Puchen ZhuPuchen Zhu received his Bachelor's degree in Engineering from Guangdong University of Technology, Guangzhou, China. he is currently a Master of Philosophy student at the Mechanical and Automation Engineering department at the Chinese University of HongKong (CUHK). his research interests include Robotics, Medical Robotics, and Robotic Modeling and Control.Yonglong HeYonglong He is a student pursuing a master's degress in Mechanical Engineering from the School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou, China. his research interests include Robotics, Compliant mechanism.
{"title":"Trajectory tracking of robot manipulator with adaptive fuzzy second-order super-twisting sliding mode control","authors":"Dachang Zhu, Puchen Zhu, Yonglong He","doi":"10.1080/01691864.2023.2270792","DOIUrl":"https://doi.org/10.1080/01691864.2023.2270792","url":null,"abstract":"AbstractTo solve the influence of uncertainties such as unmodeled errors and external disturbances on the trajectory tracking accuracy of the end-effector of a robot manipulator, a novel fuzzy super-twisting second-order sliding mode control method is proposed in this paper. Based on the dynamic model of the robot manipulator, a second-order sliding mode control algorithm is proposed by using the super-twisting to determine the non-singular terminal sliding manifold. An adaptive fuzzy algorithm is presented to compensate for the super-twisting second-order sliding mode control system for handling the chattering and overestimating the controller gains. The stability of the proposed controller is verified by the Lyapunov stability theory. Simulation and experimental results show that the proposed control method can enable the robot to track the trajectory accurately under complex and uncertain conditions and effectively suppress the chattering phenomenon of the system.Keywords: Robot manipulatortrajectory trackingadaptive fuzzy super-twisting algorithmsecond-order sliding mode control Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThe author would like to express his gratitude for the support of the University Scientific Research Project of Education Bureau of Guangzhou Municipality under Grant 202032821 and the Guangzhou City School Joint Project under Grant SL2023A03J00681.Notes on contributorsDachang ZhuDachang Zhu received the B.S. degree in mechanics design and the M.S. degree in theoretical mechanics from the Jiangxi University of Technology and Science, Ganzhou, China, in 1996 and 1999, respectively, the Ph.D. degree in mechanical engineering from Beijing Jiaotong University, Beijing, China, in 2008, and the Postdoctor in mechanical engineering from the South China University of Technology, Guangzhou, China, in 2012. He currently works as full professor with the School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou, China. His research interests include topology optimization theory and applications in compliant mechanisms, robotics, and feedback control of the dynamic systems.Puchen ZhuPuchen Zhu received his Bachelor's degree in Engineering from Guangdong University of Technology, Guangzhou, China. he is currently a Master of Philosophy student at the Mechanical and Automation Engineering department at the Chinese University of HongKong (CUHK). his research interests include Robotics, Medical Robotics, and Robotic Modeling and Control.Yonglong HeYonglong He is a student pursuing a master's degress in Mechanical Engineering from the School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou, China. his research interests include Robotics, Compliant mechanism.","PeriodicalId":7261,"journal":{"name":"Advanced Robotics","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136376456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Empathy in human-robot conversations aims to endow the robot with the ability to comprehend user emotion and experience, and then respond to it appropriately. Generally, empathy is embodied in the aspects of both contextual understanding and affective expression, which occur when there exist content and emotion consistencies between context and response. However, previous studies only focus on either aspect. In this paper, we propose a dual variational generative model (DVG) for empathetic response generation to achieve both. Specifically, we integrate an emotion classifier and a variational autoencoder (VAE) into a dual response and context generative model to learn the emotion and content consistencies efficiently. DVG utilizes VAE to mimic the process of context/response understanding. In addition to the generative model, our model can effectively switch to another retrieval system as a fallback solution. Automatic and human evaluations on Japanese and English EmpatheticDialogue datasets demonstrate the effectiveness of our method for empathetic response generation. Furthermore, we evaluate our model's ability in general response generation, which is not specific to empathetic but also chitchatting dialogue system. GRAPHICAL ABSTRACT
{"title":"Dual variational generative model and auxiliary retrieval for empathetic response generation by conversational robot","authors":"Yahui Fu, Koji Inoue, Divesh Lala, Kenta Yamamoto, Chenhui Chu, Tatsuya Kawahara","doi":"10.1080/01691864.2023.2270577","DOIUrl":"https://doi.org/10.1080/01691864.2023.2270577","url":null,"abstract":"Empathy in human-robot conversations aims to endow the robot with the ability to comprehend user emotion and experience, and then respond to it appropriately. Generally, empathy is embodied in the aspects of both contextual understanding and affective expression, which occur when there exist content and emotion consistencies between context and response. However, previous studies only focus on either aspect. In this paper, we propose a dual variational generative model (DVG) for empathetic response generation to achieve both. Specifically, we integrate an emotion classifier and a variational autoencoder (VAE) into a dual response and context generative model to learn the emotion and content consistencies efficiently. DVG utilizes VAE to mimic the process of context/response understanding. In addition to the generative model, our model can effectively switch to another retrieval system as a fallback solution. Automatic and human evaluations on Japanese and English EmpatheticDialogue datasets demonstrate the effectiveness of our method for empathetic response generation. Furthermore, we evaluate our model's ability in general response generation, which is not specific to empathetic but also chitchatting dialogue system. GRAPHICAL ABSTRACT","PeriodicalId":7261,"journal":{"name":"Advanced Robotics","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135618418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-06DOI: 10.1080/01691864.2023.2263062
Fanta Camara, Charles Fox
Hall’s theory of proxemics established distinct spatial zones around humans where they experience comfort or discomfort when interacting with others. Our previous work proposed a new model of proxemics and trust and it showed how to generate proxemics zone sizes using simple equations from human kinematic behaviour. But like most work, this assumed that the zones are circular. In this paper, we refine this model to take the initial heading of the agent into account and find that this results in a non-circular outer boundary of the social zone. These new analytical results from a generative model form a step towards more advanced quantitative proxemics in dual agents’ interaction modelling.
{"title":"A kinematic model generates non-circular human proxemics zones","authors":"Fanta Camara, Charles Fox","doi":"10.1080/01691864.2023.2263062","DOIUrl":"https://doi.org/10.1080/01691864.2023.2263062","url":null,"abstract":"Hall’s theory of proxemics established distinct spatial zones around humans where they experience comfort or discomfort when interacting with others. Our previous work proposed a new model of proxemics and trust and it showed how to generate proxemics zone sizes using simple equations from human kinematic behaviour. But like most work, this assumed that the zones are circular. In this paper, we refine this model to take the initial heading of the agent into account and find that this results in a non-circular outer boundary of the social zone. These new analytical results from a generative model form a step towards more advanced quantitative proxemics in dual agents’ interaction modelling.","PeriodicalId":7261,"journal":{"name":"Advanced Robotics","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134944301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-06DOI: 10.1080/01691864.2023.2263046
Nicolas Gartner, Niels Montanari, Mathieu Richier, Vincent Hugel, Ramprasad Sampath
AbstractThis work presents the first results using Smoothed Particle Hydrodynamics (SPH), a mesh-free technique, to simulate underwater vehicle motion with the goal of achieving sufficient physical realism and computation time performance capabilities. The objective is not to get very accurate values for the hydrodynamic parameters, but to show that SPH can simulate hydrodynamic parameters with the same order of magnitude as the reference, in order to allow a realistic control of robots in water. First, spherical objects are simulated to check buoyancy realism, speed limit existence, and hydrodynamic parameters in comparison with reference values. Then, horizontal and vertical movements of a capsule-shape object and a real torpedo-shape underwater robot are compared. The results show that buoyancy is respected, and that spherical objects reach a speed limit in accordance with the laws of physics. In addition, added-mass is simulated with 20 % variation on average with respect to the reference and varies homothetically with respect to the object's size. In contrast, drag forces cannot not be simulated with the same level of realism without reducing the particle size, which makes the simulation last longer. SPH for underwater robotics simulation appears to be promising, and ways of further improvements are being considered.Keywords: Fluid-solid interactionunderwater roboticssmoothed particle hydrodynamicshydrodynamic parameterscomputational fluid dynamics AcknowledgmentsThis work is a result of the collaboration of Centroid LAB Inc. (Los Angeles, USA), which develops the Neutrino software, and the Laboratoire COSMER (Toulon, France).Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was partly supported by the Direction Générale de l'Armement, DGA RAPID grant in partnership with SUBSEA-TECH (Marseille, France) and ROBOPEC (Six-Fours-les-Plages, France).Notes on contributorsNicolas GartnerNicolas Gartner received his Master's degree in Engineering from SIGMA Clermont in 2016, formerly Institut Français de Mécanique Avancée, and his PhD degree from the University of Toulon in 2020. This research was conducted during his postdoc at the University of Toulon. He currently serves the Centre Technologique Méditerranéen de Métrologie. His research work is focused on underwater robotics and the evaluation of hydrodynamics performance using fluid simulation techniques like SPH.Niels MontanariNiels Montanari holds Master's degrees in computer science, applied mathematics and environmental physics, from the Bordeaux Institute of Technology, Grenoble-Alpes University and Paris-Saclay University, respectively. Since 2015, he has worked for Centroid LAB as a simulation & software engineer, performing research and development work on mesh-free particle-based methods for simulating fluid flows in various industry applications.Mathieu RichierMathieu Richier graduated from the Ecole Nationale Sup
摘要本文首次提出了利用光滑粒子流体力学(SPH)技术模拟水下航行器运动的结果，以达到足够的物理真实感和计算时间性能。目的不是为了得到非常精确的水动力参数值，而是为了表明SPH可以模拟与参考相同数量级的水动力参数，从而实现对水中机器人的真实控制。首先，对球形物体进行模拟，以检查浮力的真实感、速度限制的存在性以及与参考值进行比较的流体动力参数。然后，比较了胶囊形状物体和真实鱼雷形状水下机器人的水平和垂直运动。结果表明，该方法考虑了浮力，并且球形物体达到了符合物理定律的速度极限。此外，模拟的附加质量相对于参照物平均有20%的变化，并且相对于物体的大小有均匀的变化。相比之下，如果不减小颗粒尺寸，就不能以相同的逼真度模拟阻力，这使得模拟持续时间更长。SPH用于水下机器人模拟似乎很有前途，并且正在考虑进一步改进的方法。关键词:流固相互作用水下机器人光滑粒子流体动力学流体动力学参数计算流体动力学致谢这项工作是开发中微子软件的质心实验室公司(美国洛杉矶)和COSMER实验室(法国土伦)合作的结果。披露声明作者未报告潜在的利益冲突。这项工作的部分资金由Direction gsamnsamrale de l'Armement, DGA RAPID资助，与SUBSEA-TECH(法国马赛)和ROBOPEC(法国six - fourles - plages)合作。尼古拉斯·高德纳(nicolas Gartner)于2016年获得西格玛克莱蒙特大学(SIGMA Clermont)的工程学硕士学位，该大学前身为法国技术研究所(Institut francaais de msamcanique avancemade)，并于2020年获得土伦大学(University of Toulon)的博士学位。这项研究是他在土伦大学做博士后期间进行的。他目前服务于msamdterransamendemsamtrogie技术中心。他的研究工作主要集中在水下机器人和利用SPH等流体模拟技术评估流体动力学性能。Niels Montanari分别在波尔多理工学院、格勒诺布尔-阿尔卑斯大学和巴黎萨克雷大学获得计算机科学、应用数学和环境物理学硕士学位。自2015年以来，他一直在质心实验室工作，担任模拟和软件工程师，从事研究和开发基于无网格颗粒的方法，用于模拟各种工业应用中的流体流动。Mathieu Richier于2005年毕业于法国利摩日国立高等电子和电子技术学院，获得机电一体化学位。他于2013年在法国Clermont Auvergne大学获得博士学位。2014年加入土伦大学，任副教授。他的研究主要集中在水动力建模、观测算法的实现和水下航行器机电系统的开发。Vincent HugelVincent Hugel分别于1999年和2007年获得巴黎第六大学机器人博士学位和法国凡尔赛大学机器人研究学位。自2014年以来，他是土伦大学的正教授，并领导法国土伦的Cosmer实验室(机器人系统设计)，在那里他正在进行自主机器人和水下航行器的研究。Ramprasad Sampath, 1992年获得Birla Institute of Technology and science的计算机科学硕士学位，1995年获得University of South Carolina的计算机科学硕士学位。1995年至2013年期间，他在不同的视觉效果工作室担任技术总监和高级技术总监。自2013年以来，他一直在Centroid LAB工作，现任首席执行官兼研发总监。主要研究方向为粒子流体模拟和火灾模拟。
{"title":"Can smoothed particle hydrodynamics simulate physically realistic movements of underwater vehicles?","authors":"Nicolas Gartner, Niels Montanari, Mathieu Richier, Vincent Hugel, Ramprasad Sampath","doi":"10.1080/01691864.2023.2263046","DOIUrl":"https://doi.org/10.1080/01691864.2023.2263046","url":null,"abstract":"AbstractThis work presents the first results using Smoothed Particle Hydrodynamics (SPH), a mesh-free technique, to simulate underwater vehicle motion with the goal of achieving sufficient physical realism and computation time performance capabilities. The objective is not to get very accurate values for the hydrodynamic parameters, but to show that SPH can simulate hydrodynamic parameters with the same order of magnitude as the reference, in order to allow a realistic control of robots in water. First, spherical objects are simulated to check buoyancy realism, speed limit existence, and hydrodynamic parameters in comparison with reference values. Then, horizontal and vertical movements of a capsule-shape object and a real torpedo-shape underwater robot are compared. The results show that buoyancy is respected, and that spherical objects reach a speed limit in accordance with the laws of physics. In addition, added-mass is simulated with 20 % variation on average with respect to the reference and varies homothetically with respect to the object's size. In contrast, drag forces cannot not be simulated with the same level of realism without reducing the particle size, which makes the simulation last longer. SPH for underwater robotics simulation appears to be promising, and ways of further improvements are being considered.Keywords: Fluid-solid interactionunderwater roboticssmoothed particle hydrodynamicshydrodynamic parameterscomputational fluid dynamics AcknowledgmentsThis work is a result of the collaboration of Centroid LAB Inc. (Los Angeles, USA), which develops the Neutrino software, and the Laboratoire COSMER (Toulon, France).Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was partly supported by the Direction Générale de l'Armement, DGA RAPID grant in partnership with SUBSEA-TECH (Marseille, France) and ROBOPEC (Six-Fours-les-Plages, France).Notes on contributorsNicolas GartnerNicolas Gartner received his Master's degree in Engineering from SIGMA Clermont in 2016, formerly Institut Français de Mécanique Avancée, and his PhD degree from the University of Toulon in 2020. This research was conducted during his postdoc at the University of Toulon. He currently serves the Centre Technologique Méditerranéen de Métrologie. His research work is focused on underwater robotics and the evaluation of hydrodynamics performance using fluid simulation techniques like SPH.Niels MontanariNiels Montanari holds Master's degrees in computer science, applied mathematics and environmental physics, from the Bordeaux Institute of Technology, Grenoble-Alpes University and Paris-Saclay University, respectively. Since 2015, he has worked for Centroid LAB as a simulation & software engineer, performing research and development work on mesh-free particle-based methods for simulating fluid flows in various industry applications.Mathieu RichierMathieu Richier graduated from the Ecole Nationale Sup","PeriodicalId":7261,"journal":{"name":"Advanced Robotics","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135352319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-05DOI: 10.1080/01691864.2023.2263064
Hiroki Dobashi, Koki Ogawa, Mizuho Shibata, Wataru Uemura, Yasuyoshi Yokokohji
AbstractA task-board task is a task in which robots perform a set of fundamental assembly operations related to the assembly of a specific product such as inserting a part into another part, tightening bolts and nuts, attaching flexible parts, etc. In the task-board task competition in the industrial robotics category of the World Robot Summit (WRS) 2018, 15 teams from around the world performed the task. In our previous work, we observed recorded videos of the competition frame by frame and manually investigated the performance of robotic systems of the top four teams, focusing only on whether robots contact target parts. In this paper, we introduce frame sets to represent mainly contact states between robots, tools, or jigs and target parts to be handled for such a frame-by-frame analysis of the recorded videos, and based on the frame sets, we analyze the performance of robotic assembly systems of other teams as well as the top four teams in the task-board task competition in the WRS 2018, considering the usage of tools or jigs.Keywords: Task-board taskrobotic assembly systemWorld Robot Summit AcknowledgementsThe authors would like to express their gratitude to everyone involved in the design and operation of the Assembly Challenge in the Industrial Robotics Category, the WRS 2018, especially those who greatly contributed to the competition as referees and health and safety inspectors.Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 Note that strictly speaking, analysis with films is called ‘film analysis’.2 The video is not open to the public unlike those available on the official YouTube channel of the WRS [Citation28].3 In the task-board task competition, not the tightening torque but only the amount of rotation of each of these parts was evaluated. In that case, the tightening of parts 7-1, 7-2, 8, 12, and 13 can be achieved even by human hand itself (without using the tools) whereas part 11 is hard to handle due to its small size.4 No clear picture of the electric screwdriver can be extracted from either the video recorded by the competition committee or [Citation28], but a clear one is given in [Citation29].5 In [Citation22], a cylindrical part with a diameter which is equal to or greater than its length is treated as a non-cylindrical part.Additional informationFundingThis work is based on results obtained from a project commissioned by the New Energy and Industrial Technology Development Organization (NEDO)[P17004] and was supported by Japan Society for the Promotion of Science (JSPS) KAKENHI Grant Number JP21K03978.Notes on contributorsHiroki DobashiHiroki Dobashi received the BS, MS, and PhD degrees in mechanical engineering from Kyoto University in 2007, 2009, and 2012, respectively. From 2012 to 2013, he was a contract assistant at School of Science and Technology, Kwansei Gakuin University. From 2013 to 2017, he was an assistant professor in the Department of Robotics, Faculty of Science an
{"title":"Analysis of the performance of robotic assembly systems considering the usage of tools or jigs for the task-board task in the WRS 2018","authors":"Hiroki Dobashi, Koki Ogawa, Mizuho Shibata, Wataru Uemura, Yasuyoshi Yokokohji","doi":"10.1080/01691864.2023.2263064","DOIUrl":"https://doi.org/10.1080/01691864.2023.2263064","url":null,"abstract":"AbstractA task-board task is a task in which robots perform a set of fundamental assembly operations related to the assembly of a specific product such as inserting a part into another part, tightening bolts and nuts, attaching flexible parts, etc. In the task-board task competition in the industrial robotics category of the World Robot Summit (WRS) 2018, 15 teams from around the world performed the task. In our previous work, we observed recorded videos of the competition frame by frame and manually investigated the performance of robotic systems of the top four teams, focusing only on whether robots contact target parts. In this paper, we introduce frame sets to represent mainly contact states between robots, tools, or jigs and target parts to be handled for such a frame-by-frame analysis of the recorded videos, and based on the frame sets, we analyze the performance of robotic assembly systems of other teams as well as the top four teams in the task-board task competition in the WRS 2018, considering the usage of tools or jigs.Keywords: Task-board taskrobotic assembly systemWorld Robot Summit AcknowledgementsThe authors would like to express their gratitude to everyone involved in the design and operation of the Assembly Challenge in the Industrial Robotics Category, the WRS 2018, especially those who greatly contributed to the competition as referees and health and safety inspectors.Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 Note that strictly speaking, analysis with films is called ‘film analysis’.2 The video is not open to the public unlike those available on the official YouTube channel of the WRS [Citation28].3 In the task-board task competition, not the tightening torque but only the amount of rotation of each of these parts was evaluated. In that case, the tightening of parts 7-1, 7-2, 8, 12, and 13 can be achieved even by human hand itself (without using the tools) whereas part 11 is hard to handle due to its small size.4 No clear picture of the electric screwdriver can be extracted from either the video recorded by the competition committee or [Citation28], but a clear one is given in [Citation29].5 In [Citation22], a cylindrical part with a diameter which is equal to or greater than its length is treated as a non-cylindrical part.Additional informationFundingThis work is based on results obtained from a project commissioned by the New Energy and Industrial Technology Development Organization (NEDO)[P17004] and was supported by Japan Society for the Promotion of Science (JSPS) KAKENHI Grant Number JP21K03978.Notes on contributorsHiroki DobashiHiroki Dobashi received the BS, MS, and PhD degrees in mechanical engineering from Kyoto University in 2007, 2009, and 2012, respectively. From 2012 to 2013, he was a contract assistant at School of Science and Technology, Kwansei Gakuin University. From 2013 to 2017, he was an assistant professor in the Department of Robotics, Faculty of Science an","PeriodicalId":7261,"journal":{"name":"Advanced Robotics","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134973946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-04DOI: 10.1080/01691864.2023.2257264
Keiko Ochi, Koji Inoue, Divesh Lala, Tatsuya Kawahara, Hirokazu Kumazaki
AbstractIn this paper, we investigate the usefulness of an attentive-listening robot in psychiatric daycare, an outpatient treatment program for the rehabilitation of psychiatric disorders. The robot was developed based on counseling techniques, such as repeating words that the user has said. It can also generate backchannels as a listening behavior during user utterance. Conversation experiments have been conducted to evaluate whether the robot can provide effective activities in this setting. The robot attentively listened to 18 daycare attendees talking about their recent memorable events for up to 3 min. The results showed that the conversation increased self-rated arousal. The impressions of the robot showed that talking with the robot was more conversable than with strangers and more useful as a talking partner than a friend. The subjects also had a positive impression about whether they would keep the robot in their homes. A linear regression analysis indicates that the frequency of the robot's assessment responses and backchannels positively affect pleasure improvement. The findings may pave the way for utilizing this kind of robots that people can talk to easily without hesitation or excessive consideration.Keywords: Communicative robotattentive listeningmental healthpsychiatric daycarespeech analysis AcknowledgmentsWe thank Dr. Yosuke Maeda and Ms. Sawa Maeda for their supervision of the experiments. We also thank Ms. Yumi Onishi, Ms. Reina Ōki and Mr. Hiroshi Notsu for the support to their experiments and insightful discussions based on their knowledge and experience in psychiatric daycare.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis study was supported by JSPS KAKENHI (19H05691) and JST Moonshot R&D Grant Number JPMJMS2011.Notes on contributorsKeiko OchiKeiko Ochi received PhD in Graduate School of Information Science and Technology, University of Tokyo, Japan. Currently, her research interests include assistive technology and speech signal processing.Koji InoueKoji Inoue received his MS and PhD degrees in informatics in 2015 and 2018 from Kyoto University, Japan. He is currently an assistant professor at Graduate School of Informatics, Kyoto University, and was a research fellow of the Japan Society for the Promotion of Science (JSPS) from 2015 to 2018. His research interests include spoken dialogue systems, speech signal processing, multimodal interaction, and conversational robots. He is a member of IEEE and ACM.Divesh LalaDivesh Lala received PhD in Graduate School of Informatics in 2015, from Kyoto University, Kyoto, Japan. Currently, he is a researcher in Graduate School of Informatics, Kyoto University. His research interests include human–robot interaction and multimodal signal processing.Tatsuya KawaharaTatsuya Kawahara received BE in 1987, ME in 1989, and PhD in 1995, all in information science, from Kyoto University, Kyoto, Japan. From 1995 to 1996, he
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