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dc.contributor.authorHawkins, Kelsey P.
dc.contributor.authorBansal, Shray
dc.contributor.authorVo, Nam
dc.contributor.authorBobick, Aaron F.
dc.date.accessioned2014-02-17T19:07:30Z
dc.date.available2014-02-17T19:07:30Z
dc.date.issued2013-11
dc.identifier.citationHawkins, K., S. Bansal, N. Vo, and A.F. Bobick , “Modeling structured activity to support human-robot collaboration in the presence of task and sensor uncertainty,” IROS Workshop on Cognitive Robotics Systems, 2013.en_US
dc.identifier.urihttp://hdl.handle.net/1853/50907
dc.description©2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
dc.descriptionPresented at the IROS Workshop on Cognitive Robotics Systems held in conjunction with the IEEE/RSJ International Conference on Intelligent Robots and Systems, November 3-8, 2013 at Tokyo Big Sight, Japan.
dc.description.abstractA representation for structured activities is developed that allows a robot to probabilistically infer which task actions a human is currently performing and to predict which future actions will be executed and when they will occur. The goal is to enable a robot to anticipate collaborative actions in the presence of uncertain sensing and task ambiguity. The system can represent multi-path tasks where the task variations may contain partially ordered actions or even optional actions that may be skipped altogether. The task is represented by an AND-OR tree structure from which a probabilistic graphical model is constructed. Inference methods for that model are derived that support a planning and execution system for the robot that attempts to minimize a cost function based upon expected human idle time. We demonstrate the theory in both simulation and actual human-robot performance of a two-waybranch assembly task. In particular we show that the inference model can robustly anticipate the actions of the human even in the presence of unreliable or noisy detections because of its integration of all its sensing information along with knowledge of task structure.en_US
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectHuman operator robot collaborationen_US
dc.subjectStructured activitiesen_US
dc.subjectComputer visionen_US
dc.titleModeling structured activity to support human-robot collaboration in the presence of task and sensor uncertaintyen_US
dc.typeProceedingsen_US
dc.typePost-printen_US
dc.contributor.corporatenameGeorgia Institute of Technology. Institute for Robotics and Intelligent Machines
dc.contributor.corporatenameGeorgia Institute of Technology. School of Interactive Computing
dc.publisher.originalInstitute of Electrical and Electronics Engineers


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