Massachusetts Institute of Technology
Recognizing actions using embodiment & empathy
Abstract
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.
Degree
thesis:*- 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 and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- McIntyre, Robert Louis
- Advisor dc:contributor.advisor
-
- Patrick H. Winston.
Subjects
dc:subject × 1Rights
dc:rights- 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
- Language dc:language.iso
- eng
Identifiers
dc:identifier.*- Handle dc:identifier.uri
- http://hdl.handle.net/1721.1/91697
- OAI identifier oai:identifier
- oai:dspace.mit.edu:1721.1/91697