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Scalable, physics-aware 6D pose estimation for robot manipulation

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TitleInfo
Title
Scalable, physics-aware 6D pose estimation for robot manipulation
Name (type = personal)
NamePart (type = family)
Mitash
NamePart (type = given)
Chaitanya
NamePart (type = date)
1991-
DisplayForm
Chaitanya Mitash
Role
RoleTerm (authority = RULIB)
author
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Boularias
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Abdeslam
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Abdeslam Boularias
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Advisory Committee
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chair
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Bekris
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Kostas
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Kostas Bekris
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Advisory Committee
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co-chair
Name (type = personal)
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Elgammal
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Ahmed
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Ahmed Elgammal
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Advisory Committee
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internal member
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Christensen
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Henrik
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Henrik Christensen
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Advisory Committee
Role
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outside member
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Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
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NamePart
School of Graduate Studies
Role
RoleTerm (authority = RULIB)
school
TypeOfResource
Text
Genre (authority = marcgt)
theses
Genre (authority = ExL-Esploro)
ETD doctoral
OriginInfo
DateCreated (qualifier = exact); (encoding = w3cdtf); (keyDate = yes)
2020
DateOther (type = degree); (qualifier = exact); (encoding = w3cdtf)
2020-10
Language
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English
Abstract (type = abstract)
Robot Manipulation often depend on some form of pose estimation to represent the state of the world and allow decision making both at the task-level and for motion or grasp planning. Recent progress in deep learning gives hope for a pose estimation solution that could generalize over textured and texture-less objects, objects with or without distinctive shape properties, and under different lighting conditions and clutter scenarios. Nevertheless, it gives rise to a new set of challenges such as the painful task of acquiring large-scale labeled training datasets and of dealing with their stochastic output over unforeseen scenarios that are not captured by the training. This restricts the scalability of such pose estimation solutions in robot manipulation tasks that often deal with a variety of objects and changing environments.

The thesis first describes an automatic data generation and learning framework to address the scalability challenge. Learning is bootstrapped by generating labeled data via physics simulation and rendering. Then it self-improves over time by acquiring and labeling real-world images via a search-based pose estimation process. The thesis proposes algorithms to generate and validate object poses online based on the objects’ geometry and based on the physical consistency of their scene-level interactions. These algorithms provide robustness even when there exists a domain gap between the synthetic training and the real test scenarios. Finally, the thesis proposes a manipulation planning framework that goes beyond model-based pose estimation. By utilizing a dynamic object representation, this integrated perception and manipulation framework can efficiently solve the task of picking unknown objects and placing them in a constrained space.

The algorithms are evaluated over real-world robot manipulation experiments and over large-scale public datasets. The results indicate the usefulness of physical constraints in both the training and the online estimation phase. Moreover, the proposed framework, while only utilizing simulated data can obtain robust estimation in challenging scenarios such as densely-packed bins and clutter where other approaches suffer as a result of large occlusion and ambiguities due to similar looking texture-less surfaces.
Subject (authority = local)
Topic
6D pose estimation
Subject (authority = RUETD)
Topic
Computer Science
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_11248
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application/pdf
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text/xml
Extent
1 online resource (xvi, 191 pages) : illustrations
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
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TitleInfo
Title
School of Graduate Studies Electronic Theses and Dissertations
Identifier (type = local)
rucore10001600001
Location
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NjNbRU
Identifier (type = doi)
doi:10.7282/t3-s5fk-ht60
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Rights

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The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Mitash
GivenName
Chaitanya
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2020-09-29 23:30:52
AssociatedEntity
Name
Chaitanya Mitash
Role
Copyright holder
Affiliation
Rutgers University. School of Graduate Studies
AssociatedObject
Type
License
Name
Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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Technical

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DateCreated (point = end); (encoding = w3cdtf); (qualifier = exact)
2020-09-30T03:21:27
DateCreated (point = end); (encoding = w3cdtf); (qualifier = exact)
2020-09-30T03:21:27
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