2026-09-19T15:02:00Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1036762026-06-16T18:53:55Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
Leslie Pack Kaelbling and Tomás Lozano-Pérez.
Wong, Lawson L. S. (Lawson Lok Sang)
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2016-07-18T19:11:55Z
2016-07-18T19:11:55Z
2016
2016
http://hdl.handle.net/1721.1/103676
953527811
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.
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 113-120).
Mobile-manipulation robots performing service tasks in human-centric indoor environments have long been a dream for developers of autonomous agents. Tasks such as cooking and cleaning typically involve interaction with the environment, hence robots need to know relevant aspects of their spatial surroundings. However, service robots typically have little prior information about their environment, unlike industrial robots in structured environments. Even if this information was given initially, due to the involvement of other agents (e.g., humans adding/moving/removing objects), uncertainty in the complete state of the world is inevitable over time. Additionally, most information about the world is irrelevant to any particular task at hand. Mobile-manipulation robots therefore need to continuously perform the task of state estimation, using perceptual information to maintain a representation of the state, and its uncertainty, of task-relevant aspects of the world. Because indoor tasks frequently require interacting with objects, objects should be given critical emphasis in spatial representations for service robots. Compared to occupancy grids and feature-based maps that have been used traditionally in navigation and mapping, object-based representations are still in their infancy. By definition, mobile-manipulation robots are capable of moving in and interacting with the world. Hence, at the very least, such robots need to know about the physical occupancy of space and potential targets of interaction (i.e., objects). In this thesis, I propose a representation based on objects, their 'semantic' attributes (task-relevant properties such as type and pose), and their geometric realizations in the physical world.
by Lawson L.S. Wong.
Ph.D.
120 pages
eng
Massachusetts Institute of Technology
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.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Learning the state of the world : object-based world modeling for mobile-manipulation robots
Object-based world modeling for mobile-manipulation robots
Thesis
application/pdf
Publication
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unknown
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<Abstract>Mobile-manipulation robots performing service tasks in human-centric indoor environments have long been a dream for developers of autonomous agents. Tasks such as cooking and cleaning typically involve interaction with the environment, hence robots need to know relevant aspects of their spatial surroundings. However, service robots typically have little prior information about their environment, unlike industrial robots in structured environments. Even if this information was given initially, due to the involvement of other agents (e.g., humans adding/moving/removing objects), uncertainty in the complete state of the world is inevitable over time. Additionally, most information about the world is irrelevant to any particular task at hand. Mobile-manipulation robots therefore need to continuously perform the task of state estimation, using perceptual information to maintain a representation of the state, and its uncertainty, of task-relevant aspects of the world. Because indoor tasks frequently require interacting with objects, objects should be given critical emphasis in spatial representations for service robots. Compared to occupancy grids and feature-based maps that have been used traditionally in navigation and mapping, object-based representations are still in their infancy. By definition, mobile-manipulation robots are capable of moving in and interacting with the world. Hence, at the very least, such robots need to know about the physical occupancy of space and potential targets of interaction (i.e., objects). In this thesis, I propose a representation based on objects, their 'semantic' attributes (task-relevant properties such as type and pose), and their geometric realizations in the physical world.</Abstract>
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