2026-09-18T22:55:28Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1178232022-01-13T07:54:01Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Leslie P. Kaelbling and Tomás Lozano-Pérez.
Chitnis, Rohan
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2018-09-17T14:51:08Z
2018-09-17T14:51:08Z
2018
2018
http://hdl.handle.net/1721.1/117823
1051460759
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 77-79).
In partially observed environments, it can be useful for a human to provide the robot with declarative information that augments its direct sensory observations. For instance, given a robot on a search-and-rescue mission, a human operator might suggest locations of interest. We provide a representation for the robot's internal knowledge that supports efficient combination of raw sensory information with high-level declarative information presented in a formal language. Computational efficiency is achieved by dynamically selecting an appropriate factoring of the belief state, combining aspects of the belief when they are correlated through information and separating them when they are not. This strategy works in open domains, in which the set of possible objects is not known in advance, and provides significant improvements in inference time, leading to more efficient planning for complex partially observable tasks. We validate our approach experimentally in two open-domain planning problems: a 2D discrete gridworld task and a 3D continuous cooking task.
by Rohan Chitnis.
S.M.
79 pages
eng
Massachusetts Institute of Technology
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Integrating human-provided information into belief state representation using dynamic factorization
Thesis
application/pdf
false
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="ba0ecc97-ee3e-4558-b629-12129f38fd5b">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Language>eng</Language>
<Title>Integrating human-provided information into belief state representation using dynamic factorization</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2018</PublicationDate>
<Authors>
<Author>
<DisplayName>Chitnis, Rohan</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>http://dspace.mit.edu/handle/1721.1/7582</License>
<Keyword>Electrical Engineering and Computer Science.</Keyword>
<Abstract>In partially observed environments, it can be useful for a human to provide the robot with declarative information that augments its direct sensory observations. For instance, given a robot on a search-and-rescue mission, a human operator might suggest locations of interest. We provide a representation for the robot's internal knowledge that supports efficient combination of raw sensory information with high-level declarative information presented in a formal language. Computational efficiency is achieved by dynamically selecting an appropriate factoring of the belief state, combining aspects of the belief when they are correlated through information and separating them when they are not. This strategy works in open domains, in which the set of possible objects is not known in advance, and provides significant improvements in inference time, leading to more efficient planning for complex partially observable tasks. We validate our approach experimentally in two open-domain planning problems: a 2D discrete gridworld task and a 3D continuous cooking task.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>