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dc.contributor.advisorKamal Youcef-Toumi.en_US
dc.contributor.authorCui, Yingnan, S.M. Massachusetts Institute of Technologyen_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Mechanical Engineering.en_US
dc.date.accessioned2019-02-05T15:59:49Z
dc.date.available2019-02-05T15:59:49Z
dc.date.copyright2018en_US
dc.date.issued2018en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/120233
dc.descriptionThesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2018.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 95-97).en_US
dc.description.abstractThe robot phone disassembly task is difficult in many ways: It has requirements on high precision, high speed, and should be general to all types of cell phones. Previous works on robot learning from demonstration are hardly applicable due to the complexity of teaching, huge amounts of data and difficulty in generalization. To tackle these problems, we try to learn from videos and extract useful information for the robot. To reduce the amounts of data we need to process, we generate a mask for the video and observe only the region of interest. Inspired by the idea that spatio-temporal interest point (STIP) detector may give meaningful points such as the contact point between the tool and the part, we design a new method of detecting STIPs based on optical flow. We also design a new descriptor by modifying the histogram of optical flow. The STIP detector and descriptor together can make sure that the features are invariant to scale, rotation and noises. Using the modified histogram of optical flow descriptor, we show that even without considering raw pixels of the original video, we can achieve pretty good classification results.en_US
dc.description.statementofresponsibilityby Yingnan Cui.en_US
dc.format.extent97 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectMechanical Engineering.en_US
dc.titleOn learning from videos/en_US
dc.typeThesisen_US
dc.description.degreeS.M.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mechanical Engineering.en_US
dc.identifier.oclc1083124360en_US


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