Learning articulated motions from visual demonstration

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.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2014

Author dc:creator

• Pillai, Sudeep

Advisor dc:contributor.advisor

• Seth Teller.

• Electrical Engineering and Computer Science.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/89861

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/89861