Show simple item record

dc.contributor.advisorPatrick H. Winston.en_US
dc.contributor.authorMcIntyre, Robert Louisen_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2014-11-24T16:16:19Z
dc.date.available2014-11-24T16:16:19Z
dc.date.copyright2014en_US
dc.date.issued2014en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/91697
dc.descriptionThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.en_US
dc.descriptionThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.en_US
dc.descriptionCataloged from student-submitted PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 83-85).en_US
dc.description.abstractHere I demonstrate the power of using embodied artificial intelligence to attack the action recognition problem, which is the challenge of recognizing actions performed by a creature given limited data about the creature's actions, such as a video recording. I solve this problem in the case of a worm-like creature performing actions such as curling and wiggling. To attack the action recognition problem, I developed a computational model of empathy (EMPATH) which allows me to recognize actions using simple, embodied representations of actions (which require rich sensory data), even when that sensory data is not actually available. The missing sense data is imagined by combining previous experiences gained from unsupervised free play. The worm is a five-segment creature equipped with touch, proprioception, and muscle tension senses. It recognizes actions using only proprioception data. In order to build this empathic, action-recognizing system, I created a program called CORTEX, which is a complete platform for embodied AI research. It provides multiple senses for simulated creatures, including vision, touch, proprioception, muscle tension, and hearing. Each of these senses provides a wealth of parameters that are biologically inspired. CORTEX is able to simulate any number of creatures and senses, and provides facilities for easily modeling and creating new creatures. As a research platform it is more complete than any other system currently available.en_US
dc.description.statementofresponsibilityby Robert Louis McIntyre.en_US
dc.format.extent85 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.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.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleRecognizing actions using embodiment & empathyen_US
dc.title.alternativeRecognizing actions using embodiment and empathyen_US
dc.typeThesisen_US
dc.description.degreeM. Eng.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.identifier.oclc894249493en_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record