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dc.contributor.authorPark, Hae Won
dc.date.accessioned2016-04-20T15:17:14Z
dc.date.available2016-04-20T15:17:14Z
dc.date.issued2012
dc.identifier.citationPark, H. W. (2012). Task-Learning Policies for Collaborative Task Solving in Human-Robot Interaction. Proceedings of the 14th ACM international Conference on Multimodal Interaction (ICMI '12), pp. 341-344.en_US
dc.identifier.isbn978-1-4503-1467-1
dc.identifier.urihttp://hdl.handle.net/1853/54748
dc.descriptionCopyright ©2012 ACMen_US
dc.descriptionDOI: 10.1145/2388676.2388752
dc.description.abstractThe objective of this doctoral research is to design multimodal task-learning policies for a robotic system that targets the exchange of task rules between humans and robots. This objective is achieved through a collaborative task application during human-robot interaction where the two partners learn a task from each other and accomplish a shared goal. As a first step, a method to model human-action primitives using a pattern-recognition technique is presented. Next, algorithms are developed to generate turn-taking strategies in response to human task behaviors. The contribution of this work is in engaging robots with humans in collaborative play task by modeling statistical patterns of play behaviors and reusing previously learned knowledge to reduce the decision process. Here, results of previous work are presented, and remaining works including deploying a physically embodied agent and developing an evaluation platform are outlined.en_US
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectCollaborative task learning and solvingen_US
dc.subjectHuman-robot interactionen_US
dc.titleTask-Learning Policies for Collaborative Task Solving in Human-Robot Interactionen_US
dc.typeProceedingsen_US
dc.contributor.corporatenameGeorgia Institute of Technology. Institute for Robotics and Intelligent Machinesen_US
dc.contributor.corporatenameGeorgia Institute of Technology. Human-Automation Systems Laben_US
dc.contributor.corporatenameGeorgia Institute of Technology. School of Electrical and Computer Engineeringen_US
dc.publisher.originalAssociation for Computing Machinery
dc.identifier.doi10.1145/2388676.2388752
dc.embargo.termsnullen_US


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