{"title":"UHTP: A User-Aware Hierarchical Task Planning Framework for Communication-Free, Mutually-Adaptive Human-Robot Collaboration","authors":"Kartik Ramachandruni, Cassandra Kent, Sonia Chernova","doi":"10.1145/3623387","DOIUrl":null,"url":null,"abstract":"Collaborative human-robot task execution approaches require mutual adaptation, allowing both the human and robot partners to take active roles in action selection and role assignment to achieve a single shared goal. Prior works have utilized a leader-follower paradigm in which either agent must follow the actions specified by the other agent. We introduce the User-aware Hierarchical Task Planning (UHTP) framework, a communication-free human-robot collaborative approach for adaptive execution of multi-step tasks that moves beyond the leader-follower paradigm. Specifically, our approach enables the robot to observe the human, perform actions that support the human’s decisions, and actively select actions that maximize the expected efficiency of the collaborative task. In turn, the human chooses actions based on their observation of the task and the robot, without being dictated by a scheduler or the robot. We evaluate UHTP both in simulation and in a human subjects experiment of a collaborative drill assembly task. Our results show that UHTP achieves more efficient task plans and shorter task completion times than non-adaptive baselines across a wide range of human behaviors, that interacting with a UHTP-controlled robot reduces the human’s cognitive workload, and that humans prefer to work with our adaptive robot over a fixed-policy alternative.","PeriodicalId":36515,"journal":{"name":"ACM Transactions on Human-Robot Interaction","volume":null,"pages":null},"PeriodicalIF":4.2000,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on Human-Robot Interaction","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3623387","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ROBOTICS","Score":null,"Total":0}
引用次数: 0
Abstract
Collaborative human-robot task execution approaches require mutual adaptation, allowing both the human and robot partners to take active roles in action selection and role assignment to achieve a single shared goal. Prior works have utilized a leader-follower paradigm in which either agent must follow the actions specified by the other agent. We introduce the User-aware Hierarchical Task Planning (UHTP) framework, a communication-free human-robot collaborative approach for adaptive execution of multi-step tasks that moves beyond the leader-follower paradigm. Specifically, our approach enables the robot to observe the human, perform actions that support the human’s decisions, and actively select actions that maximize the expected efficiency of the collaborative task. In turn, the human chooses actions based on their observation of the task and the robot, without being dictated by a scheduler or the robot. We evaluate UHTP both in simulation and in a human subjects experiment of a collaborative drill assembly task. Our results show that UHTP achieves more efficient task plans and shorter task completion times than non-adaptive baselines across a wide range of human behaviors, that interacting with a UHTP-controlled robot reduces the human’s cognitive workload, and that humans prefer to work with our adaptive robot over a fixed-policy alternative.
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.