Pub Date : 2021-08-08DOI: 10.1109/RO-MAN50785.2021.9515485
Miguel Faria, Francisco S. Melo, A. Paiva
In this work we explore implicit communication between humans and robots—through movement—in multi-party (or multi-user) interactions. In particular, we investigate how a robot can move to better convey its intentions using legible movements in multi-party interactions. Current research on the application of legible movements has focused on single-user interactions, causing a vacuum of knowledge regarding the impact of such movements in multi-party interactions. We propose a novel approach that extends the notion of legible motion to multi-party settings, by considering that legibility depends on all human users involved in the interaction, and should take into consideration how each of them perceives the robot’s movements from their respective points-of-view. We show, through simulation and a user study, that our proposed model of multi-user legibility leads to movements that, on average, optimize the legibility of the motion as perceived by the group of users. Our model creates movements that allow each human to more quickly and confidently understand what are the robot’s intentions, thus creating safer, clearer and more efficient interactions and collaborations.
{"title":"Understanding Robots: Making Robots More Legible in Multi-Party Interactions","authors":"Miguel Faria, Francisco S. Melo, A. Paiva","doi":"10.1109/RO-MAN50785.2021.9515485","DOIUrl":"https://doi.org/10.1109/RO-MAN50785.2021.9515485","url":null,"abstract":"In this work we explore implicit communication between humans and robots—through movement—in multi-party (or multi-user) interactions. In particular, we investigate how a robot can move to better convey its intentions using legible movements in multi-party interactions. Current research on the application of legible movements has focused on single-user interactions, causing a vacuum of knowledge regarding the impact of such movements in multi-party interactions. We propose a novel approach that extends the notion of legible motion to multi-party settings, by considering that legibility depends on all human users involved in the interaction, and should take into consideration how each of them perceives the robot’s movements from their respective points-of-view. We show, through simulation and a user study, that our proposed model of multi-user legibility leads to movements that, on average, optimize the legibility of the motion as perceived by the group of users. Our model creates movements that allow each human to more quickly and confidently understand what are the robot’s intentions, thus creating safer, clearer and more efficient interactions and collaborations.","PeriodicalId":6854,"journal":{"name":"2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN)","volume":"52 1","pages":"1031-1036"},"PeriodicalIF":0.0,"publicationDate":"2021-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90595610","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 : 2021-08-08DOI: 10.1109/RO-MAN50785.2021.9515449
H. Miwa, Kentaro Watanabe, Sakiko Itoh, T. Ikeuchi, Tomoko Wakui
The shortage of human resources in Japan has become a serious social issue in nursing care services owing to the rapid increase in the demand and the declining working population. Although the use of intelligent technologies such as robots, AI, and IT is expected to offer solutions, little progress has been made in the introduction and extensive use of these technologies in nursing care services. In this study, we aimed to clarify the factors that increase the preference for technology use in nursing care services from the perspective of trust. We conducted an online survey on attitudes towards nursing care and technologies in Japan, and statistically analyzed the relationships among the preference for technology use in nursing care, trust in technologies, and personal characteristics. The results revealed that those with high trust in intelligent technologies were more likely to prefer the use of such technologies in nursing care services.
{"title":"Analysis on interaction experiences with intelligent technology and preference of technology use in nursing care*","authors":"H. Miwa, Kentaro Watanabe, Sakiko Itoh, T. Ikeuchi, Tomoko Wakui","doi":"10.1109/RO-MAN50785.2021.9515449","DOIUrl":"https://doi.org/10.1109/RO-MAN50785.2021.9515449","url":null,"abstract":"The shortage of human resources in Japan has become a serious social issue in nursing care services owing to the rapid increase in the demand and the declining working population. Although the use of intelligent technologies such as robots, AI, and IT is expected to offer solutions, little progress has been made in the introduction and extensive use of these technologies in nursing care services. In this study, we aimed to clarify the factors that increase the preference for technology use in nursing care services from the perspective of trust. We conducted an online survey on attitudes towards nursing care and technologies in Japan, and statistically analyzed the relationships among the preference for technology use in nursing care, trust in technologies, and personal characteristics. The results revealed that those with high trust in intelligent technologies were more likely to prefer the use of such technologies in nursing care services.","PeriodicalId":6854,"journal":{"name":"2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN)","volume":"39 1","pages":"559-564"},"PeriodicalIF":0.0,"publicationDate":"2021-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87653757","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 : 2021-08-08DOI: 10.1109/RO-MAN50785.2021.9515416
N. DePalma, J. Hodgins
Current models of gesture synthesis focus primarily on a speech signal to synthesize gestures. In this paper, we take a critical look at this approach from the point of view of gesture’s tendency to disambiguate the verbal component of the expression. We identify and contribute an analysis of three challenge factors for these models: 1) synthesizing gesture in the presence of ambiguous utterances seems to be a overwhelmingly useful case for gesture production yet is not at present supported by present day models of gesture generation, 2) finding the best f-formation to convey spatial gestural information like gesturing directions makes a significant difference for everyday users and must be taken into account, and 3) assuming that captured human motion is a plentiful and easy source for retargeting gestural motion may not yet take into account the readability of gestures under kinematically constrained feasibility spaces.Recent approaches to generate gesture for agents[1] and robots [2] treat gesture as co-speech that is strictly dependent on verbal utterances. Evidence suggests that gesture selection may leverage task context so it is not dependent on verbal utterance only. This effect is particularly evident when attempting to generate gestures from ambiguous verbal utterances (e.g. "You do this when you get to the fork in the road"). Decoupling this strict dependency may allow gesture to be synthesized for the purpose of clarification of the ambiguous verbal utterance.
{"title":"Factor exploration of gestural stroke choice in the context of ambiguous instruction utterances: challenges to synthesizing semantic gesture from speech alone","authors":"N. DePalma, J. Hodgins","doi":"10.1109/RO-MAN50785.2021.9515416","DOIUrl":"https://doi.org/10.1109/RO-MAN50785.2021.9515416","url":null,"abstract":"Current models of gesture synthesis focus primarily on a speech signal to synthesize gestures. In this paper, we take a critical look at this approach from the point of view of gesture’s tendency to disambiguate the verbal component of the expression. We identify and contribute an analysis of three challenge factors for these models: 1) synthesizing gesture in the presence of ambiguous utterances seems to be a overwhelmingly useful case for gesture production yet is not at present supported by present day models of gesture generation, 2) finding the best f-formation to convey spatial gestural information like gesturing directions makes a significant difference for everyday users and must be taken into account, and 3) assuming that captured human motion is a plentiful and easy source for retargeting gestural motion may not yet take into account the readability of gestures under kinematically constrained feasibility spaces.Recent approaches to generate gesture for agents[1] and robots [2] treat gesture as co-speech that is strictly dependent on verbal utterances. Evidence suggests that gesture selection may leverage task context so it is not dependent on verbal utterance only. This effect is particularly evident when attempting to generate gestures from ambiguous verbal utterances (e.g. \"You do this when you get to the fork in the road\"). Decoupling this strict dependency may allow gesture to be synthesized for the purpose of clarification of the ambiguous verbal utterance.","PeriodicalId":6854,"journal":{"name":"2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN)","volume":"9 1","pages":"102-109"},"PeriodicalIF":0.0,"publicationDate":"2021-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75514707","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 : 2021-08-08DOI: 10.1109/RO-MAN50785.2021.9515357
Tomoki Inaishi, Mizuki Enoki, H. Noguchi
In this study, we developed a new rule-based dialog system that includes both turn-taking detection and response-switching functions. The traditional rule-based part allows the system user to design and customize topics and conversation transitions easily. In addition to the traditional rule-based part, the turn-taking detection function can detect the human speaker’s continuous speech and control the system’s speech response according to the detected continuous speech. This mechanism enables conversations without interfering with the speech of the human speaker. The turn-taking detection was created using a deep neural network (DNN). Using the turn-taking detection result, the response switching function can change three types of response transitions: a back-channeling response, topic changing, and normal conversation transition.To confirm that our developed functions could improve the impressions of rule-based dialog systems, an experiment comparing the dialog system with and without our developed functions was conducted. The system with both turn-taking detection and response switching functions provided a relatively better impression to the participants. Although some improvements are needed for the dialog system, the results suggest that the use of a turn-taking detection function may enable a relatively smooth conversation even with a rule-based dialog system.
{"title":"A Voice Dialog System without Interfering with Human Speech Based on Turn-taking Detection","authors":"Tomoki Inaishi, Mizuki Enoki, H. Noguchi","doi":"10.1109/RO-MAN50785.2021.9515357","DOIUrl":"https://doi.org/10.1109/RO-MAN50785.2021.9515357","url":null,"abstract":"In this study, we developed a new rule-based dialog system that includes both turn-taking detection and response-switching functions. The traditional rule-based part allows the system user to design and customize topics and conversation transitions easily. In addition to the traditional rule-based part, the turn-taking detection function can detect the human speaker’s continuous speech and control the system’s speech response according to the detected continuous speech. This mechanism enables conversations without interfering with the speech of the human speaker. The turn-taking detection was created using a deep neural network (DNN). Using the turn-taking detection result, the response switching function can change three types of response transitions: a back-channeling response, topic changing, and normal conversation transition.To confirm that our developed functions could improve the impressions of rule-based dialog systems, an experiment comparing the dialog system with and without our developed functions was conducted. The system with both turn-taking detection and response switching functions provided a relatively better impression to the participants. Although some improvements are needed for the dialog system, the results suggest that the use of a turn-taking detection function may enable a relatively smooth conversation even with a rule-based dialog system.","PeriodicalId":6854,"journal":{"name":"2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN)","volume":"9 2","pages":"820-825"},"PeriodicalIF":0.0,"publicationDate":"2021-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91452368","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 : 2021-08-08DOI: 10.1109/RO-MAN50785.2021.9515538
Bjarke Kristian Maigaard Kjær Pedersen, Bente Charlotte Weigelin, J. C. Larsen, Jacob Nielsen
With this paper we present the first – to our knowledge – systematic review on how to use Educational Robotics to foster girls’ interest in STEM. This is a research area essential to broadening the participation across the genders in the much-needed STEM workforce, whose size is currently held back by a significant gender disparity. In the review, 13 (quasi-)experimental studies were selected for synthesis, from a total of 1093 results found across multiple search queries applied to six scientific databases. When synthesizing the results and findings from the included studies, four major categories of research interest were identified. On the basis hereof, a list of recommendations, which are readily implementable in most curriculums for both compulsory education and extracurricular activities, was established. The recommendations revolve around: The choice of technology, applied contextualization, approaches to teaching, and gender compositions. In addition, we discuss the current extent of research on the topic, which shows indications of becoming more active in recent years, while likewise discussing the reviews implications for future research directions.
{"title":"Using educational robotics to foster girls’ interest in STEM: A systematic review","authors":"Bjarke Kristian Maigaard Kjær Pedersen, Bente Charlotte Weigelin, J. C. Larsen, Jacob Nielsen","doi":"10.1109/RO-MAN50785.2021.9515538","DOIUrl":"https://doi.org/10.1109/RO-MAN50785.2021.9515538","url":null,"abstract":"With this paper we present the first – to our knowledge – systematic review on how to use Educational Robotics to foster girls’ interest in STEM. This is a research area essential to broadening the participation across the genders in the much-needed STEM workforce, whose size is currently held back by a significant gender disparity. In the review, 13 (quasi-)experimental studies were selected for synthesis, from a total of 1093 results found across multiple search queries applied to six scientific databases. When synthesizing the results and findings from the included studies, four major categories of research interest were identified. On the basis hereof, a list of recommendations, which are readily implementable in most curriculums for both compulsory education and extracurricular activities, was established. The recommendations revolve around: The choice of technology, applied contextualization, approaches to teaching, and gender compositions. In addition, we discuss the current extent of research on the topic, which shows indications of becoming more active in recent years, while likewise discussing the reviews implications for future research directions.","PeriodicalId":6854,"journal":{"name":"2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN)","volume":"45 1","pages":"865-872"},"PeriodicalIF":0.0,"publicationDate":"2021-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76995994","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 : 2021-08-08DOI: 10.1109/RO-MAN50785.2021.9515486
Vincent Fortineau, M. Makarov, P. Rodríguez-Ayerbe, I. Siegler
The estimation of the human endpoint impedance interacting with a physical environment provides modelling insights both for the field of human movement science and for the design of innovative controllers for collaborative robotics based on physical human-robot interaction. Most of the existing human impedance estimation methods described in the literature rely on controlled environments closer to a laboratory than to an industrial setting. In this paper, a force perturbation method is proposed without any specific requirements on neither the force nor the position trajectories and without additional sensors placed on the human. The method is illustrated through an experimental study on a benchmark interaction task. The task was selected to be sufficiently variable for preventing the use of average trajectories and the perturbations used for the estimations are sufficiently low amplitude and short to not deteriorate the human performance in the task. The obtained impedance model parameters are in adequacy with the ones found in the literature, suggesting the validity of the proposed approach. The low constraints on the experimental settings make it applicable even out of the lab, for example, in human-robot collaboration in manufacturing environments, to adjust the robot behaviour to changes in the state of the operator (fatigue, stress) or to adjust to a variety of human operators with different interaction strategies.
{"title":"Towards a seamless experimental protocol for human arm impedance estimation in an interactive dynamic task","authors":"Vincent Fortineau, M. Makarov, P. Rodríguez-Ayerbe, I. Siegler","doi":"10.1109/RO-MAN50785.2021.9515486","DOIUrl":"https://doi.org/10.1109/RO-MAN50785.2021.9515486","url":null,"abstract":"The estimation of the human endpoint impedance interacting with a physical environment provides modelling insights both for the field of human movement science and for the design of innovative controllers for collaborative robotics based on physical human-robot interaction. Most of the existing human impedance estimation methods described in the literature rely on controlled environments closer to a laboratory than to an industrial setting. In this paper, a force perturbation method is proposed without any specific requirements on neither the force nor the position trajectories and without additional sensors placed on the human. The method is illustrated through an experimental study on a benchmark interaction task. The task was selected to be sufficiently variable for preventing the use of average trajectories and the perturbations used for the estimations are sufficiently low amplitude and short to not deteriorate the human performance in the task. The obtained impedance model parameters are in adequacy with the ones found in the literature, suggesting the validity of the proposed approach. The low constraints on the experimental settings make it applicable even out of the lab, for example, in human-robot collaboration in manufacturing environments, to adjust the robot behaviour to changes in the state of the operator (fatigue, stress) or to adjust to a variety of human operators with different interaction strategies.","PeriodicalId":6854,"journal":{"name":"2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN)","volume":"23 1","pages":"31-36"},"PeriodicalIF":0.0,"publicationDate":"2021-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87293197","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 : 2021-08-08DOI: 10.1109/RO-MAN50785.2021.9515365
Connor Esterwood, L. Robert
Trust is vital to promoting human and robot collaboration, but like human teammates, robots make mistakes that undermine trust. As a result, a human’s perception of his or her robot teammate’s trustworthiness can dramatically decrease [1], [2], [3], [4]. Trustworthiness consists of three distinct dimensions: ability (i.e. competency), benevolence (i.e. concern for the trustor) and integrity (i.e. honesty) [5], [6]. Taken together, decreases in trustworthiness decreases trust in the robot [7]. To address this, we conducted a 2 (high vs. low anthropomorphism) x 4 (trust repair strategies) between-subjects experiment. Preliminary results of the first 164 participants (between 19 and 24 per cell) highlight which repair strategies are effective relative to ability, integrity and benevolence and the robot’s anthropomorphism. Overall, this paper contributes to the HRI trust repair literature.
{"title":"Do You Still Trust Me? Human-Robot Trust Repair Strategies","authors":"Connor Esterwood, L. Robert","doi":"10.1109/RO-MAN50785.2021.9515365","DOIUrl":"https://doi.org/10.1109/RO-MAN50785.2021.9515365","url":null,"abstract":"Trust is vital to promoting human and robot collaboration, but like human teammates, robots make mistakes that undermine trust. As a result, a human’s perception of his or her robot teammate’s trustworthiness can dramatically decrease [1], [2], [3], [4]. Trustworthiness consists of three distinct dimensions: ability (i.e. competency), benevolence (i.e. concern for the trustor) and integrity (i.e. honesty) [5], [6]. Taken together, decreases in trustworthiness decreases trust in the robot [7]. To address this, we conducted a 2 (high vs. low anthropomorphism) x 4 (trust repair strategies) between-subjects experiment. Preliminary results of the first 164 participants (between 19 and 24 per cell) highlight which repair strategies are effective relative to ability, integrity and benevolence and the robot’s anthropomorphism. Overall, this paper contributes to the HRI trust repair literature.","PeriodicalId":6854,"journal":{"name":"2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN)","volume":"8 1","pages":"183-188"},"PeriodicalIF":0.0,"publicationDate":"2021-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91333040","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 : 2021-08-08DOI: 10.1109/RO-MAN50785.2021.9515526
Yue (Sophie) Guo, Rohit Jena, Dana Hughes, M. Lewis, K. Sycara
To build an agent providing assistance to human rescuers in an urban search and rescue task, it is crucial to understand not only human actions but also human beliefs that may influence the decision to take these actions. Developing data-driven models to predict a rescuer’s strategies for navigating the environment and triaging victims requires costly data collection and training for each new environment of interest. Transfer learning approaches can be used to mitigate this challenge, allowing a model trained on a source environment/task to generalize to a previously unseen target environment/task with few training examples. In this paper, we investigate transfer learning (a) from a source environment with smaller number of types of injured victims to one with larger number of victim injury classes and (b) from a smaller and simpler environment to a larger and more complex one for navigation strategy. Inspired by hierarchical organization of human spatial cognition, we used graph division to represent spatial knowledge, and Transfer Learning Diffusion Convo-lutional Recurrent Neural Network (TL-DCRNN), a spatial and temporal graph-based recurrent neural network suitable for transfer learning, to predict navigation. To abstract the rescue strategy from a rescuer’s field-of-view stream, we used attention-based LSTM networks. We experimented on various transfer learning scenarios and evaluated the performance using mean average error. Results indicated our assistant agent can improve predictive accuracy and learn target tasks faster when equipped with transfer learning methods.
{"title":"Transfer Learning for Human Navigation and Triage Strategies Prediction in a Simulated Urban Search and Rescue Task","authors":"Yue (Sophie) Guo, Rohit Jena, Dana Hughes, M. Lewis, K. Sycara","doi":"10.1109/RO-MAN50785.2021.9515526","DOIUrl":"https://doi.org/10.1109/RO-MAN50785.2021.9515526","url":null,"abstract":"To build an agent providing assistance to human rescuers in an urban search and rescue task, it is crucial to understand not only human actions but also human beliefs that may influence the decision to take these actions. Developing data-driven models to predict a rescuer’s strategies for navigating the environment and triaging victims requires costly data collection and training for each new environment of interest. Transfer learning approaches can be used to mitigate this challenge, allowing a model trained on a source environment/task to generalize to a previously unseen target environment/task with few training examples. In this paper, we investigate transfer learning (a) from a source environment with smaller number of types of injured victims to one with larger number of victim injury classes and (b) from a smaller and simpler environment to a larger and more complex one for navigation strategy. Inspired by hierarchical organization of human spatial cognition, we used graph division to represent spatial knowledge, and Transfer Learning Diffusion Convo-lutional Recurrent Neural Network (TL-DCRNN), a spatial and temporal graph-based recurrent neural network suitable for transfer learning, to predict navigation. To abstract the rescue strategy from a rescuer’s field-of-view stream, we used attention-based LSTM networks. We experimented on various transfer learning scenarios and evaluated the performance using mean average error. Results indicated our assistant agent can improve predictive accuracy and learn target tasks faster when equipped with transfer learning methods.","PeriodicalId":6854,"journal":{"name":"2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN)","volume":"13 1","pages":"784-791"},"PeriodicalIF":0.0,"publicationDate":"2021-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83687794","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 : 2021-08-08DOI: 10.1109/RO-MAN50785.2021.9515504
M. Brandão
Visions have an important role in guiding and legitimizing technical research, as well as contributing to expectations of the general public towards technologies. In this paper we analyze technical robotics papers published between 1998 and 2019 to identify themes, trends and issues with the visions and values promoted by robotics research. In particular, we identify the themes of robotics visions and implicitly normative visions; and we quantify the relative presence of a variety of values and applications within technical papers. We conclude with a discussion of the language of robotics visions, marginalized visions and values, and possible paths forward for the robotics community to better align practice with societal interest. We also discuss implications and future work suggestions for Responsible Robotics and HRI research.
{"title":"Normative roboticists: the visions and values of technical robotics papers","authors":"M. Brandão","doi":"10.1109/RO-MAN50785.2021.9515504","DOIUrl":"https://doi.org/10.1109/RO-MAN50785.2021.9515504","url":null,"abstract":"Visions have an important role in guiding and legitimizing technical research, as well as contributing to expectations of the general public towards technologies. In this paper we analyze technical robotics papers published between 1998 and 2019 to identify themes, trends and issues with the visions and values promoted by robotics research. In particular, we identify the themes of robotics visions and implicitly normative visions; and we quantify the relative presence of a variety of values and applications within technical papers. We conclude with a discussion of the language of robotics visions, marginalized visions and values, and possible paths forward for the robotics community to better align practice with societal interest. We also discuss implications and future work suggestions for Responsible Robotics and HRI research.","PeriodicalId":6854,"journal":{"name":"2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN)","volume":"21 1","pages":"671-677"},"PeriodicalIF":0.0,"publicationDate":"2021-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84142022","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 : 2021-08-08DOI: 10.1109/RO-MAN50785.2021.9515453
Mridul Agarwal, Glebys T. Gonzalez, Mythra V. Balakuntala, Md Masudur Rahman, V. Aggarwal, R. Voyles, Yexiang Xue, J. Wachs
In austere environments, teleoperated surgical robots could save the lives of critically injured patients if they can perform complex surgical maneuvers under limited communication bandwidth. The bandwidth requirement is reduced by transferring atomic surgical actions (referred to as “surgemes”) instead of the low-level kinematic information. While such a policy reduces the bandwidth requirement, it requires accurate recognition of the surgemes. In this paper, we demonstrate that transfer learning across surgical tasks can boost the performance of surgeme recognition. This is demonstrated by using a network pre-trained with peg-transfer data from Yumi robot to learn classification on debridement on data from Taurus robot. Using a pre-trained network improves the classification accuracy achieves a classification accuracy of 76% with only 8 sequences in target domain, which is 22.5% better than no-transfer scenario. Additionally, ablations on transfer learning indicate that transfer learning requires 40% less data compared to no-transfer to achieve same classification accuracy. Further, the convergence rate of the transfer learning setup is significantly higher than the no-transfer setup trained only on the target domain.
{"title":"Dexterous Skill Transfer between Surgical Procedures for Teleoperated Robotic Surgery","authors":"Mridul Agarwal, Glebys T. Gonzalez, Mythra V. Balakuntala, Md Masudur Rahman, V. Aggarwal, R. Voyles, Yexiang Xue, J. Wachs","doi":"10.1109/RO-MAN50785.2021.9515453","DOIUrl":"https://doi.org/10.1109/RO-MAN50785.2021.9515453","url":null,"abstract":"In austere environments, teleoperated surgical robots could save the lives of critically injured patients if they can perform complex surgical maneuvers under limited communication bandwidth. The bandwidth requirement is reduced by transferring atomic surgical actions (referred to as “surgemes”) instead of the low-level kinematic information. While such a policy reduces the bandwidth requirement, it requires accurate recognition of the surgemes. In this paper, we demonstrate that transfer learning across surgical tasks can boost the performance of surgeme recognition. This is demonstrated by using a network pre-trained with peg-transfer data from Yumi robot to learn classification on debridement on data from Taurus robot. Using a pre-trained network improves the classification accuracy achieves a classification accuracy of 76% with only 8 sequences in target domain, which is 22.5% better than no-transfer scenario. Additionally, ablations on transfer learning indicate that transfer learning requires 40% less data compared to no-transfer to achieve same classification accuracy. Further, the convergence rate of the transfer learning setup is significantly higher than the no-transfer setup trained only on the target domain.","PeriodicalId":6854,"journal":{"name":"2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN)","volume":"83 1","pages":"1236-1242"},"PeriodicalIF":0.0,"publicationDate":"2021-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83794477","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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