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dc.contributor.authorHermans, Tucker
dc.date.accessioned2019-10-08T19:07:00Z
dc.date.available2019-10-08T19:07:00Z
dc.date.issued2019-09-11
dc.identifier.urihttp://hdl.handle.net/1853/61905
dc.descriptionPresented on September 11th, 2019 at 12:15 p.m.-1:15 p.m. in the Marcus Nanotechnology Building, Rooms 1116-1118, Georgia Tech.en_US
dc.descriptionTucker Hermans is an assistant professor in the School of Computing at the University of Utah, where he is a founding member of the University of Utah Robotics Center. He was a visiting professor at NVIDIA Research during summer 2019. Hermans is a recipient of the NSF CAREER award and the 3M Non-Tenured Faculty Award. His research has been nominated for multiple conference paper awards, including winning the Best Medical Robotics Paper Award at ICRA 2017. Previously, Hermans was a postdoctoral fellow at TU Darmstadt working with Jan Peters. He attended Georgia Tech from 2009 to 2014, in the School of Interactive Computing where he earned his Ph.D. in Robotics under the supervision of Aaron Bobick and Jim Rehg. At Georgia Tech he earned a M.Sc. in Computer Science. Additionally, Hermans received an A.B. in German and Computer Science from Bowdoin College in 2009.en_US
dc.descriptionRuntime: 58:58 minutesen_US
dc.description.abstractMulti-fingered hands offer autonomous robots increased dexterity, versatility, and stability over simple two-fingered grippers. Naturally, this increased ability comes with increased complexity in planning and executing manipulation actions. As such, I propose combining model-based planning with learned components to improve over purely data-driven or purely-model based approaches to manipulation. This talk examines multi-fingered autonomous manipulation when the robot has only partial knowledge of the object of interest. I will first present results on planning multi-fingered grasps for novel objects using a learned neural network. I will then present our approach to planning in-hand manipulation tasks when dynamic properties of objects are not known. I will conclude with a discussion of our ongoing and future research to further unify these two approaches.en_US
dc.format.extent58:58 minutes
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.relation.ispartofseriesIRIM Seminar Seriesen_US
dc.subjectMachine learningen_US
dc.subjectManipulationen_US
dc.subjectRoboticsen_US
dc.titleImproving Multi-Fingered Robot Manipulation by Unifying Learning and Planningen_US
dc.typeLectureen_US
dc.typeVideoen_US
dc.contributor.corporatenameGeorgia Institute of Technology. Institute for Robotics and Intelligent Machinesen_US
dc.contributor.corporatenameUniversity of Utahen_US


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  • IRIM Seminar Series [124]
    Each semester a core seminar series is announced featuring guest speakers from around the world and from varying backgrounds in robotics.

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