Mohammad Samin Yasar, Md Mofijul Islam, Tariq Iqbal
{"title":"印记:使用多模态上下文的团队中的交互式动态感知运动预测","authors":"Mohammad Samin Yasar, Md Mofijul Islam, Tariq Iqbal","doi":"10.1145/3626954","DOIUrl":null,"url":null,"abstract":"Robots are moving from working in isolation to working with humans as a part of human-robot teams. In such situations, they are expected to work with multiple humans and need to understand and predict the team members’ actions. To address this challenge, in this work, we introduce IMPRINT, a multi-agent motion prediction framework that models the interactional dynamics and incorporates the multimodal context (e.g., data from RGB and depth sensors and skeleton joint positions) to accurately predict the motion of all the agents in a team. In IMPRINT, we propose an Interaction module that can extract the intra-agent and inter-agent dynamics before fusing them to obtain the interactional dynamics. Furthermore, we propose a Multimodal Context module that incorporates multimodal context information to improve multi-agent motion prediction. We evaluated IMPRINT by comparing its performance on human-human and human-robot team scenarios against state-of-the-art methods. The results suggest that IMPRINT outperformed all other methods over all evaluated temporal horizons. Additionally, we provide an interpretation of how IMPRINT incorporates the multimodal context information from all the modalities during multi-agent motion prediction. The superior performance of IMPRINT provides a promising direction to integrate motion prediction with robot perception and enable safe and effective human-robot collaboration.","PeriodicalId":36515,"journal":{"name":"ACM Transactions on Human-Robot Interaction","volume":null,"pages":null},"PeriodicalIF":4.2000,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"IMPRINT: Interactional Dynamics-aware Motion Prediction in Teams using Multimodal Context\",\"authors\":\"Mohammad Samin Yasar, Md Mofijul Islam, Tariq Iqbal\",\"doi\":\"10.1145/3626954\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Robots are moving from working in isolation to working with humans as a part of human-robot teams. In such situations, they are expected to work with multiple humans and need to understand and predict the team members’ actions. To address this challenge, in this work, we introduce IMPRINT, a multi-agent motion prediction framework that models the interactional dynamics and incorporates the multimodal context (e.g., data from RGB and depth sensors and skeleton joint positions) to accurately predict the motion of all the agents in a team. In IMPRINT, we propose an Interaction module that can extract the intra-agent and inter-agent dynamics before fusing them to obtain the interactional dynamics. Furthermore, we propose a Multimodal Context module that incorporates multimodal context information to improve multi-agent motion prediction. We evaluated IMPRINT by comparing its performance on human-human and human-robot team scenarios against state-of-the-art methods. The results suggest that IMPRINT outperformed all other methods over all evaluated temporal horizons. Additionally, we provide an interpretation of how IMPRINT incorporates the multimodal context information from all the modalities during multi-agent motion prediction. The superior performance of IMPRINT provides a promising direction to integrate motion prediction with robot perception and enable safe and effective human-robot collaboration.\",\"PeriodicalId\":36515,\"journal\":{\"name\":\"ACM Transactions on Human-Robot Interaction\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.2000,\"publicationDate\":\"2023-10-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM Transactions on Human-Robot Interaction\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3626954\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ROBOTICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on Human-Robot Interaction","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3626954","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ROBOTICS","Score":null,"Total":0}
IMPRINT: Interactional Dynamics-aware Motion Prediction in Teams using Multimodal Context
Robots are moving from working in isolation to working with humans as a part of human-robot teams. In such situations, they are expected to work with multiple humans and need to understand and predict the team members’ actions. To address this challenge, in this work, we introduce IMPRINT, a multi-agent motion prediction framework that models the interactional dynamics and incorporates the multimodal context (e.g., data from RGB and depth sensors and skeleton joint positions) to accurately predict the motion of all the agents in a team. In IMPRINT, we propose an Interaction module that can extract the intra-agent and inter-agent dynamics before fusing them to obtain the interactional dynamics. Furthermore, we propose a Multimodal Context module that incorporates multimodal context information to improve multi-agent motion prediction. We evaluated IMPRINT by comparing its performance on human-human and human-robot team scenarios against state-of-the-art methods. The results suggest that IMPRINT outperformed all other methods over all evaluated temporal horizons. Additionally, we provide an interpretation of how IMPRINT incorporates the multimodal context information from all the modalities during multi-agent motion prediction. The superior performance of IMPRINT provides a promising direction to integrate motion prediction with robot perception and enable safe and effective human-robot collaboration.
期刊介绍:
ACM Transactions on Human-Robot Interaction (THRI) is a prestigious Gold Open Access journal that aspires to lead the field of human-robot interaction as a top-tier, peer-reviewed, interdisciplinary publication. The journal prioritizes articles that significantly contribute to the current state of the art, enhance overall knowledge, have a broad appeal, and are accessible to a diverse audience. Submissions are expected to meet a high scholarly standard, and authors are encouraged to ensure their research is well-presented, advancing the understanding of human-robot interaction, adding cutting-edge or general insights to the field, or challenging current perspectives in this research domain.
THRI warmly invites well-crafted paper submissions from a variety of disciplines, encompassing robotics, computer science, engineering, design, and the behavioral and social sciences. The scholarly articles published in THRI may cover a range of topics such as the nature of human interactions with robots and robotic technologies, methods to enhance or enable novel forms of interaction, and the societal or organizational impacts of these interactions. The editorial team is also keen on receiving proposals for special issues that focus on specific technical challenges or that apply human-robot interaction research to further areas like social computing, consumer behavior, health, and education.