Learning articulated motions from visual demonstration
Name
890151925-MIT.pdf
Description
Full printable version
Size
32.73 MB
Format
Adobe PDF
Checksum (MD5)
dc5c4fb8f0838cf7e515dc8900b4a463
Author(s)
Pillai, Sudeep
Advisor(s)
Seth Teller.
Date Issued
2014
Publisher
Massachusetts Institute of Technology
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.
Description
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).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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