國立虎尾科技大學 |

Pretrained Representations for Embodied AI.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Pretrained Representations for Embodied AI./
作者:	Sax, Alexander.
面頁冊數:	1 online resource (159 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-03, Section: B.
Contained By:	Dissertations Abstracts International85-03B.
標題:	Computer engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9798380382403

Pretrained Representations for Embodied AI.
Sax, Alexander.

Pretrained Representations for Embodied AI. - 1 online resource (159 pages)

Source: Dissertations Abstracts International, Volume: 85-03, Section: B.

Thesis (Ph.D.)--University of California, Berkeley, 2023.

Includes bibliographical references

The world is messy and imperfect, unstructured and complex, and nonetheless we must still accomplish the basic behaviors necessary for survival. It is for this purpose, ecologically relevant behavior, that vision evolved 500-600 million years ago.This thesis is about how learn representations of the visual world that are useful for the types of behaviors we might want an embodied AI system to do. In the first part of this thesis, we systematically study how bottlenecking visual inputs through different pretrained representations affects the ability of a robot to learn different atomic navigation skills (Chapter 2) and manipulation skills (Chapter 3) through trial-and-error. The main finding is that the appropriate pretrained representation greatly improves the sample efficiency for skill acquisition, and greatly improves the generalization of the learned skill. In the second part of the thesis, we use the lessons learned in order to improve the accuracy of the representations in a larger variety of contexts (indoors, outdoors, tabletop settings, and so on). In Chapter 4 we do this through adding cross-prediction consistency objectives. In Chapter 5 we do this by leveraging vast amounts of 3D data available on the internet and from a robot's prior experience.The methods are primarily developed for the purpose of vision and action, but many of the ideas are general and could work for other sensory modalities and behaviors.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798380382403Subjects--Topical Terms:

569006
Computer engineering.
Subjects--Index Terms:

Computational ethologyIndex Terms--Genre/Form:

554714
Electronic books.

Pretrained Representations for Embodied AI.
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500 $a Advisor: Malik, Jitendra;Zamir, Amir.
502 $a Thesis (Ph.D.)--University of California, Berkeley, 2023.
504 $a Includes bibliographical references
520 $a The world is messy and imperfect, unstructured and complex, and nonetheless we must still accomplish the basic behaviors necessary for survival. It is for this purpose, ecologically relevant behavior, that vision evolved 500-600 million years ago.This thesis is about how learn representations of the visual world that are useful for the types of behaviors we might want an embodied AI system to do. In the first part of this thesis, we systematically study how bottlenecking visual inputs through different pretrained representations affects the ability of a robot to learn different atomic navigation skills (Chapter 2) and manipulation skills (Chapter 3) through trial-and-error. The main finding is that the appropriate pretrained representation greatly improves the sample efficiency for skill acquisition, and greatly improves the generalization of the learned skill. In the second part of the thesis, we use the lessons learned in order to improve the accuracy of the representations in a larger variety of contexts (indoors, outdoors, tabletop settings, and so on). In Chapter 4 we do this through adding cross-prediction consistency objectives. In Chapter 5 we do this by leveraging vast amounts of 3D data available on the internet and from a robot's prior experience.The methods are primarily developed for the purpose of vision and action, but many of the ideas are general and could work for other sensory modalities and behaviors.
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538 $a Mode of access: World Wide Web
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653 $a Pretrained representations
653 $a Robotics
653 $a Transfer learning
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