2026-09-18T22:18:40Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/898612022-01-13T07:54:01Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Seth Teller.
Pillai, Sudeep
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2014-09-19T19:38:03Z
2014-09-19T19:38:03Z
2014
2014
http://hdl.handle.net/1721.1/89861
890151925
Thesis: S.M. in Computer Science and Engineering, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
35
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 94-98).
Robots operating autonomously in household environments must be capable of interacting with articulated objects on a daily basis. They should be able to infer each object's underlying kinematic linkages purely by observing its motion during manipulation. This work proposes a framework that enables robots to learn the articulation in objects from user-provided demonstrations, using RGB-D sensors. We introduce algorithms that combine concepts in sparse feature tracking, motion segmentation, object pose estimation, and articulation learning, to develop our proposed framework. Additionally, our methods can predict the motion of previously seen articulated objects in future encounters. We present experiments that demonstrate the ability of our method, given RGB-D data, to identify, analyze and predict the articulation of a number of everyday objects within a human-occupied environment.
by Sudeep Pillai.
S.M. in Computer Science and Engineering
98 pages
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Learning articulated motions from visual demonstration
Thesis
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<Abstract>Robots operating autonomously in household environments must be capable of interacting with articulated objects on a daily basis. They should be able to infer each object's underlying kinematic linkages purely by observing its motion during manipulation. This work proposes a framework that enables robots to learn the articulation in objects from user-provided demonstrations, using RGB-D sensors. We introduce algorithms that combine concepts in sparse feature tracking, motion segmentation, object pose estimation, and articulation learning, to develop our proposed framework. Additionally, our methods can predict the motion of previously seen articulated objects in future encounters. We present experiments that demonstrate the ability of our method, given RGB-D data, to identify, analyze and predict the articulation of a number of everyday objects within a human-occupied environment.</Abstract>
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