자료유형  

 학위논문

Control Number  

0016934465

International Standard Book Number  

9798380483001

Dewey Decimal Classification Number  

620

Main Entry-Personal Name  

Kurenkov, Andrey.

Publication, Distribution, etc. (Imprint  

[S.l.] : Stanford University., 2023

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2023

Physical Description  

1 online resource(132 p.)

General Note  

Source: Dissertations Abstracts International, Volume: 85-04, Section: B.

General Note  

Advisor: Finn, Chelsea;Bohg, Jeannette;Savarese, Silvio.

Dissertation Note  

Thesis (Ph.D.)--Stanford University, 2023.

Restrictions on Access Note  

This item must not be sold to any third party vendors.

Summary, Etc.  

요약Embodied agents often need to find objects to achieve downstream tasks, which makes it valuable to study solutions to the challenges posed by embodied object search. These challenges will depend on the environment the agent needs to search in: for small environments such as a bin or a shelf the primary challenges will relate to manipulation in clutter and perception in the presence occlusions, whereas in larger environments such as a room or an apartment the agent will also have to reason about where objects are likely to be and navigate to these locations. In this dissertation, I will present new methods for solving a subset of these challenges - with a focus on manipulation and reasoning - in a variety of environments, and with a variety of techniques. Specifically, I will present formulations of the problem in the context of seeking to extract an object from a bin, to reveal a hidden object on a tabletop, to predict an object's location in a house, and to remember patterns of object movement in a variety of households. For each formulation, I will present a novel learning-based approach that expands on or pushes beyond what was achieved previously. These approaches will involve object segmentation, object recognition, grasp planning, teacher-aided reinforcement learning, procedural environment generation, graph neural networks, and more. I will conclude by discussing how these methods can be refined and combined to enable embodied agents to find objects in novel real world environments.

Subject Added Entry-Topical Term  

Robots.

Subject Added Entry-Topical Term  

Access to information.

Subject Added Entry-Topical Term  

Sensitivity analysis.

Subject Added Entry-Topical Term  

Success.

Subject Added Entry-Topical Term  

Graphs.

Subject Added Entry-Topical Term  

Teachers.

Subject Added Entry-Topical Term  

Robotics.

Added Entry-Corporate Name  

Stanford University.

Host Item Entry  

Dissertations Abstracts International. 85-04B.

Host Item Entry  

Dissertation Abstract International

Electronic Location and Access  

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Control Number  

joongbu:642151