2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN)最新文献_第6页

Factor exploration of gestural stroke choice in the context of ambiguous instruction utterances: challenges to synthesizing semantic gesture from speech alone 歧义指示话语背景下手势笔画选择的因素探索:仅从言语合成语义手势的挑战

2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN) Pub Date : 2021-08-08 DOI: 10.1109/RO-MAN50785.2021.9515416

N. DePalma, J. Hodgins

{"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}

引用次数: 0

"How are you today, Panda the Robot?" – Affectiveness, Playfulness and Relatedness in Human-Robot Collaboration in the Factory Context “你今天好吗，熊猫机器人?”-工厂环境下人机协作中的情感、玩乐性和关联性

2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN) Pub Date : 2021-08-08 DOI: 10.1109/RO-MAN50785.2021.9515351

Aparajita Chowdhury, Aino Ahtinen, R. Pieters, Kaisa Väänänen

{"title":"\"How are you today, Panda the Robot?\" – Affectiveness, Playfulness and Relatedness in Human-Robot Collaboration in the Factory Context","authors":"Aparajita Chowdhury, Aino Ahtinen, R. Pieters, Kaisa Väänänen","doi":"10.1109/RO-MAN50785.2021.9515351","DOIUrl":"https://doi.org/10.1109/RO-MAN50785.2021.9515351","url":null,"abstract":"The integration of collaborative robots (cobots) is changing manufacturing and production processes in factories. When cobots are designed to be efficient, skillful and safe to interact with, workers can collaborate with them conveniently. As workers often work with cobots intensively, it is crucial to explore the user experience (UX) of cobots. The goal of our research is to explore how factory cobots could be used in ways that support pleasurable worker experiences. We adapted \"research through design\" (RtD) to conduct exploratory research on novel interactions related to affectiveness, playfulness and relatedness in human-robot collaboration (HRC) using collaborative robot arm, Panda. RtD is a method that utilizes methods and practices of design to produce new knowledge. We conducted an exploratory study with 33 participants to evaluate three HRC storyboards scenarios in two complementary remote workshops. The findings report suitability of affective and playful behavior of cobots in an industrial setting. In addition, we deduced that personality of the robot plays a crutial role in HRC.","PeriodicalId":6854,"journal":{"name":"2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN)","volume":"34 1","pages":"1089-1096"},"PeriodicalIF":0.0,"publicationDate":"2021-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75809286","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}

引用次数: 1

Branched AND/OR Graphs: Toward Flexible and Adaptable Human-Robot Collaboration 分支和/或图:走向灵活和可适应的人机协作

2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN) Pub Date : 2021-08-08 DOI: 10.1109/RO-MAN50785.2021.9515512

Hossein Karami, A. Carfì, F. Mastrogiovanni

引用次数: 0

Design and Experimental Evaluation of Foldable Robot Arms for a Holding and Installation Work: FRAHI 可折叠机器人手臂的设计与实验评价:FRAHI

2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN) Pub Date : 2021-08-08 DOI: 10.1109/RO-MAN50785.2021.9515456

Bhivraj Suthar, Y. Choi, Seul Jung

引用次数: 3

Using educational robotics to foster girls’ interest in STEM: A systematic review 使用教育机器人培养女孩对STEM的兴趣:系统回顾

2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN) Pub Date : 2021-08-08 DOI: 10.1109/RO-MAN50785.2021.9515538

Bjarke Kristian Maigaard Kjær Pedersen, Bente Charlotte Weigelin, J. C. Larsen, Jacob Nielsen

{"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}

引用次数: 3

Towards a seamless experimental protocol for human arm impedance estimation in an interactive dynamic task 面向交互式动态任务中人体手臂阻抗估计的无缝实验方案

2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN) Pub Date : 2021-08-08 DOI: 10.1109/RO-MAN50785.2021.9515486

Vincent Fortineau, M. Makarov, P. Rodríguez-Ayerbe, I. Siegler

{"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}

引用次数: 0

Do You Still Trust Me? Human-Robot Trust Repair Strategies 你还相信我吗?人机信任修复策略

2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN) Pub Date : 2021-08-08 DOI: 10.1109/RO-MAN50785.2021.9515365

Connor Esterwood, L. Robert

引用次数: 25

Transfer Learning for Human Navigation and Triage Strategies Prediction in a Simulated Urban Search and Rescue Task 模拟城市搜救任务中人类导航和分诊策略预测的迁移学习

2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN) Pub Date : 2021-08-08 DOI: 10.1109/RO-MAN50785.2021.9515526

Yue (Sophie) Guo, Rohit Jena, Dana Hughes, M. Lewis, K. Sycara

{"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}

引用次数: 5

Normative roboticists: the visions and values of technical robotics papers 规范机器人学家:技术机器人论文的愿景和价值

2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN) Pub Date : 2021-08-08 DOI: 10.1109/RO-MAN50785.2021.9515504

M. Brandão

引用次数: 6

Dexterous Skill Transfer between Surgical Procedures for Teleoperated Robotic Surgery 远程操作机器人手术过程中灵巧技能的转移

2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN) Pub Date : 2021-08-08 DOI: 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

{"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}

引用次数: 1