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dc.contributor.authorStilman, Mike
dc.contributor.authorNishiwaki, Koichi
dc.contributor.authorKagami, Satoshi
dc.date.accessioned2010-12-16T18:03:29Z
dc.date.available2010-12-16T18:03:29Z
dc.date.issued2007-11
dc.identifier.citationM. Stilman, K. Nishiwaki, S. Kagami. Learning Object Models for Humanoid Manipulation. IEEE Int. Conf on Humanoid Robotics (Humanoids ’07), November 2007.en_US
dc.identifier.urihttp://hdl.handle.net/1853/36456
dc.description© 2007 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works.en_US
dc.description.abstractWe present a successful implementation of rigid grasp manipulation for large objects moved along specified trajectories by a humanoid robot. HRP-2 manipulates tables on casters with a range of loads up to its own mass. The robot maintains dynamic balance by controlling its center of gravity to compensate for reflected forces. To achieve high performance for large objects with unspecified dynamics the robot learns a friction model for each object and applies it to torso trajectory generation. We empirically compare this method to a purely reactive strategy and show a significant increase in predictive power and stability.en_US
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectHumanoid roboten_US
dc.subjectManipulationen_US
dc.subjectHumanoid locomotionen_US
dc.titleLearning Object Models for Humanoid Manipulationen_US
dc.typeProceedingsen_US
dc.contributor.corporatenameCarnegie-Mellon University. Robotics Institute
dc.contributor.corporatenameNational Institute of Advanced Industrial Science and Technology (Japan). Digital Human Research Center
dc.publisher.originalInstitute of Electrical and Electronics Engineers


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