자료유형  

 학위논문

Control Number  

0016932381

International Standard Book Number  

9798380876766

Dewey Decimal Classification Number  

629.8

Main Entry-Personal Name  

Liu, Yu Xuan.

Publication, Distribution, etc. (Imprint  

[S.l.] : University of California, Berkeley., 2023

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2023

Physical Description  

1 online resource(85 p.)

General Note  

Source: Dissertations Abstracts International, Volume: 85-06, Section: B.

General Note  

Advisor: Abbeel, Pieter.

Dissertation Note  

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

Restrictions on Access Note  

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

Summary, Etc.  

요약Recent advances in artificial intelligence, particularly deep learning and large foundation models, have demonstrated remarkable progress. However, when applying AI to real-world robotics applications, we still face many challenges due to the diverse scenarios and objects encountered, as well as the need for high throughput and accuracy. Off-the-shelf models often fail to meet the stringent requirements for high-performing robotic applications, because they do not adequately model uncertainty that arises in the real world. Moreover, training such models require large datasets which can be expensive to annotate or not immediately applicable for robotic applications.We address these challenges by introducing a novel class of models that explicitly model and handle ambiguity in 2D and 3D perception. These models offer improved adaptability and decision-making capabilities by incorporating uncertainty estimation, better equipping robots for the dynamic nature of real-world environments. Furthermore, we explore methods of leveraging diverse data collected in robotic applications without requiring costly human annotation. We propose a self-supervised learning method that enables robots to autonomously learn from the rich information available in the diverse images they encounter during operation. This approach leads to enhanced performance and adaptability, allowing robotic systems to continuously refine their perception capabilities. We hope these contributions pave the way for more robust, adaptable, and high-performing robotic systems that excel in complex and dynamic environments, addressing the unique challenges posed by real-world robotics and bridging the gap between AI research and practical robotic applications.

Subject Added Entry-Topical Term  

Robotics.

Index Term-Uncontrolled  

Computer

Index Term-Uncontrolled  

Perception

Index Term-Uncontrolled  

Vision

Index Term-Uncontrolled  

Decision-making

Index Term-Uncontrolled  

Robots

Added Entry-Corporate Name  

University of California, Berkeley Computer Science

Host Item Entry  

Dissertations Abstracts International. 85-06B.

Host Item Entry  

Dissertation Abstract International

Electronic Location and Access  

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Control Number  

joongbu:642562