2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN)最新文献_第10页

A Robot-Delivered Program for Low-Intensity Problem-Solving Therapy for Students in Higher Education 为高等教育学生提供低强度问题解决治疗的机器人交付项目

2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN) Pub Date : 2021-08-08 DOI: 10.1109/RO-MAN50785.2021.9515532

Nicole L. Robinson, Belinda Ward, D. Kavanagh

{"title":"A Robot-Delivered Program for Low-Intensity Problem-Solving Therapy for Students in Higher Education","authors":"Nicole L. Robinson, Belinda Ward, D. Kavanagh","doi":"10.1109/RO-MAN50785.2021.9515532","DOIUrl":"https://doi.org/10.1109/RO-MAN50785.2021.9515532","url":null,"abstract":"Social robots have been used to help people to make healthy changes, and one setting that could benefit from having more support services offered includes the higher education sector. This trial involved an initial test to explore how a social robot could help to deliver a low-intensity problem-solving session for students around study-related issues and challenges. A Pepper Humanoid Robot was deployed in a student centre to help students to build a problem-solving plan on a specific issue. In the trial, 72 students gave detailed responses to session questions for issues such as procrastination, life/study balance and study workload. Students reported good ratings for emotional reaction to the robot, perceived utility, intention to use the robot again, confidence to use the robot, perceived helpfulness from the robot, likelihood to use the robot for a new higher education issue, and to recommend the robot to a friend. Robot evaluation scores were correlated with scores on perceived helpfulness of the robot and confidence to try an idea in the next week. Students who reported positive robot evaluation scores were also more willing to use the session content and rate the content as helpful. One week later, most students reported that the robot session helped them to fix their chosen issue, and that they used at least one idea from the session. Overall, this study found that a session run by a social robot could provide support for a study-related issue or challenge, and that some students did receive benefit from the session content. Future studies could include enhancements and adaptations to session length, technical refinement and capacity to address new issues during the session.","PeriodicalId":6854,"journal":{"name":"2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN)","volume":"15 1","pages":"945-950"},"PeriodicalIF":0.0,"publicationDate":"2021-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78481731","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

Exploring Children’s Beliefs for Adoption or Rejection of Domestic Social Robots* 探索儿童对家庭社交机器人的接受或拒绝的信念*

2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN) Pub Date : 2021-08-08 DOI: 10.1109/RO-MAN50785.2021.9515438

C. D. Jong, J. Peter, R. Kühne, C. L. V. Straten, Àlex Barco

引用次数: 1

Attention deep learning based model for predicting the 3D Human Body Pose using the Robot Human Handover Phases 基于注意力深度学习的机器人人机交接阶段三维人体姿态预测模型

2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN) Pub Date : 2021-08-08 DOI: 10.1109/RO-MAN50785.2021.9515402

Javier Laplaza, Albert Pumarola, F. Moreno-Noguer, A. Sanfeliu

{"title":"Attention deep learning based model for predicting the 3D Human Body Pose using the Robot Human Handover Phases","authors":"Javier Laplaza, Albert Pumarola, F. Moreno-Noguer, A. Sanfeliu","doi":"10.1109/RO-MAN50785.2021.9515402","DOIUrl":"https://doi.org/10.1109/RO-MAN50785.2021.9515402","url":null,"abstract":"This work proposes a human motion prediction model for handover operations. We use in this work, the different phases of the handover operation to improve the human motion predictions. Our attention deep learning based model takes into account the position of the robot’s End Effector (REE) and the phase in the handover operation to predict future human poses. Our model outputs a distribution of possible positions rather than one deterministic position, a key feature in order to allow robots to collaborate with humans. We provide results of the human upper body and the human right hand, also referred as Human End Effector (HEE).The attention deep learning based model has been trained and evaluated with a dataset created using human volunteers and an anthropomorphic robot, simulating handover operations where the robot is the giver and the human the receiver. For each operation, the human skeleton is obtained with an Intel RealSense D435i camera attached inside the robot’s head. The results shown a great improvement of the human’s right hand prediction and 3D body compared with other methods.","PeriodicalId":6854,"journal":{"name":"2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN)","volume":"116 1","pages":"161-166"},"PeriodicalIF":0.0,"publicationDate":"2021-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80368843","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: 8

Offline and Real-Time Implementation of a Personalized Wheelchair User Intention Detection Pipeline: A Case Study* 个性化轮椅使用者意图检测管道的离线和实时实现:一个案例研究*

2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN) Pub Date : 2021-08-08 DOI: 10.1109/RO-MAN50785.2021.9515488

M. Khalili, Kevin Ta, J. Borisoff, H. V. D. Loos

{"title":"Offline and Real-Time Implementation of a Personalized Wheelchair User Intention Detection Pipeline: A Case Study*","authors":"M. Khalili, Kevin Ta, J. Borisoff, H. V. D. Loos","doi":"10.1109/RO-MAN50785.2021.9515488","DOIUrl":"https://doi.org/10.1109/RO-MAN50785.2021.9515488","url":null,"abstract":"Pushrim-activated power-assisted wheels (PAPAWs) are assistive technologies that provide on-demand assistance to wheelchair users. PAPAWs operate based on a collaborative control scheme and require an accurate interpretation of the user’s intent to provide effective propulsion assistance. This paper investigates a user-specific intention estimation framework for wheelchair users. We used Gaussian Mixture models (GMM) to identify implicit intentions from user-pushrim interactions (i.e., input torque to the pushrims). Six clusters emerged that were associated with different phases of a stroke pattern and the intention about the desired direction of motion. GMM predictions were used as \"ground truth\" labels for further intention estimation analysis. Next, Random Forest (RF) classifiers were trained to predict user intentions. The best optimal classifier had an overall prediction accuracy of 94.7%. Finally, a Bayesian filtering (BF) algorithm was used to extract sequential dependencies of the user-pushrim measurements. The BF algorithm improved sequences of intention predictions for some wheelchair maneuvers compared to the GMM and RF predictions. The proposed intention estimation pipeline is computationally efficient and was successfully tested and used for real-time prediction of wheelchair user’s intentions. This framework provides the foundation for the development of user-specific and adaptive PAPAW controllers.","PeriodicalId":6854,"journal":{"name":"2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN)","volume":"41 1","pages":"1210-1215"},"PeriodicalIF":0.0,"publicationDate":"2021-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77708065","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: 2

Multi-modal Proactive Approaching of Humans for Human-Robot Cooperative Tasks 人机协作任务中人的多模态主动逼近

2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN) Pub Date : 2021-08-08 DOI: 10.1109/RO-MAN50785.2021.9515475

Lakshadeep Naik, Oskar Palinko, L. Bodenhagen, N. Krüger

引用次数: 4

Do You Mind if I Pass Through? Studying the Appropriate Robot Behavior when Traversing two Conversing People in a Hallway Setting* 你介意我过去吗?研究机器人在走廊中穿越两个正在交谈的人时的适当行为*

2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN) Pub Date : 2021-08-08 DOI: 10.1109/RO-MAN50785.2021.9515430

Björn Petrak, Gundula Sopper, Katharina Weitz, E. André

引用次数: 2

Human Arm Motion Prediction in Reaching Movements* 人类手臂运动预测在到达运动*

2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN) Pub Date : 2021-08-08 DOI: 10.1109/RO-MAN50785.2021.9515461

Alexander Nguyen, Biyun Xie

引用次数: 0

BabyNet: A Lightweight Network for Infant Reaching Action Recognition in Unconstrained Environments to Support Future Pediatric Rehabilitation Applications BabyNet:一个轻量级的网络，用于在不受约束的环境中识别婴儿的动作，以支持未来的儿科康复应用

2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN) Pub Date : 2021-08-08 DOI: 10.1109/RO-MAN50785.2021.9515507

Amel Dechemi, Vikarn Bhakri, Ipsita Sahin, Arjun Modi, Julya Mestas, Pamodya Peiris, Dannya Enriquez Barrundia, Elena Kokkoni, Konstantinos Karydis

{"title":"BabyNet: A Lightweight Network for Infant Reaching Action Recognition in Unconstrained Environments to Support Future Pediatric Rehabilitation Applications","authors":"Amel Dechemi, Vikarn Bhakri, Ipsita Sahin, Arjun Modi, Julya Mestas, Pamodya Peiris, Dannya Enriquez Barrundia, Elena Kokkoni, Konstantinos Karydis","doi":"10.1109/RO-MAN50785.2021.9515507","DOIUrl":"https://doi.org/10.1109/RO-MAN50785.2021.9515507","url":null,"abstract":"Action recognition is an important component to improve autonomy of physical rehabilitation devices, such as wearable robotic exoskeletons. Existing human action recognition algorithms focus on adult applications rather than pediatric ones. In this paper, we introduce BabyNet, a light-weight (in terms of trainable parameters) network structure to recognize infant reaching action from off-body stationary cameras. We develop an annotated dataset that includes diverse reaches performed while in a sitting posture by different infants in unconstrained environments (e.g., in home settings, etc.). Our approach uses the spatial and temporal connection of annotated bounding boxes to interpret onset and offset of reaching, and to detect a complete reaching action. We evaluate the efficiency of our proposed approach and compare its performance against other learning-based network structures in terms of capability of capturing temporal inter-dependencies and accuracy of detection of reaching onset and offset. Results indicate our BabyNet can attain solid performance in terms of (average) testing accuracy that exceeds that of other larger networks, and can hence serve as a light-weight data-driven framework for video-based infant reaching action recognition.","PeriodicalId":6854,"journal":{"name":"2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN)","volume":"13 1","pages":"461-467"},"PeriodicalIF":0.0,"publicationDate":"2021-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89726618","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

Enabling Robots to Adhere to Social Norms by Detecting F-Formations 通过检测f型队形使机器人遵守社会规范

2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN) Pub Date : 2021-08-08 DOI: 10.1109/RO-MAN50785.2021.9515484

A. Kollakidou, Lakshadeep Naik, Oskar Palinko, L. Bodenhagen

引用次数: 3

Validation of Robot Interactive Behaviors Through Users Emotional Perception and Their Effects on Trust 机器人交互行为的情感感知验证及其对信任的影响

2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN) Pub Date : 2021-08-08 DOI: 10.1109/RO-MAN50785.2021.9515352

Ilenia Cucciniello, S. SanGiovanni, Gianpaolo Maggi, Silvia Rossi

{"title":"Validation of Robot Interactive Behaviors Through Users Emotional Perception and Their Effects on Trust","authors":"Ilenia Cucciniello, S. SanGiovanni, Gianpaolo Maggi, Silvia Rossi","doi":"10.1109/RO-MAN50785.2021.9515352","DOIUrl":"https://doi.org/10.1109/RO-MAN50785.2021.9515352","url":null,"abstract":"When modeling the social behavior of a robot, the simulation of a specific personality or different interaction style may affect the perception of the interaction itself and the acceptability of the robot. Different interaction styles may be simulated through the use of verbal and non-verbal features that may not be easily recognized by the user as intended by the designer. For this reason, this study aimed to evaluate how three different robot interaction styles (i.e., Friendly, Neutral, and Authoritarian) were perceived by humans in the context of a robot carrying out cognitive tests. The Self-Assessment Manikin (SAM) was proposed to measure the perceived Valence, Arousal, and Dominance. We expected that a Neutral behavior is characterized by low Arousal, a Friendly by high Valence, and an Authoritarian by high Dominance. Moreover, the perception of a Socially Assistive Robot’s behavior is closely linked to trust, which is a key component to the success of any care-provider/user relationship. Hence, a Trust Perception Scale was used to explore the effect of the interaction style on trust. The results confirmed our hypothesis and showed a significant difference between each value with the others. Furthermore, we expected to obtain a higher value of trust with the Authoritarian since the performance of the users who interacted with the Authoritarian was better than the others. However, this hypothesis was not confirmed by the results.","PeriodicalId":6854,"journal":{"name":"2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN)","volume":"36 1","pages":"197-202"},"PeriodicalIF":0.0,"publicationDate":"2021-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86543922","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