자료유형  

 학위논문

Control Number  

0017163752

International Standard Book Number  

9798342107396

Dewey Decimal Classification Number  

620

Main Entry-Personal Name  

Gupta, Agrim.

Publication, Distribution, etc. (Imprint  

[S.l.] : Stanford University., 2024

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2024

Physical Description  

129 p.

General Note  

Source: Dissertations Abstracts International, Volume: 86-04, Section: B.

General Note  

Advisor: Li, Fei-Fei.

Dissertation Note  

Thesis (Ph.D.)--Stanford University, 2024.

Summary, Etc.  

요약Animals and humans display remarkable ability at building internal representations of the world and using them to simulate, evaluate and select among different possible actions. This capability is learnt primarily from observation and without any supervision. Endowing autonomous agents with similar capabilities is a fundamental challenge in machine learning. In this thesis I will explore new algorithms that enable scalable representation learning from videos via prediction, generative models of visual data and their applications to robotics.To begin, I will discuss the challenges associated with using predictive learning objectives to learn visual representations. I'll introduce a simple predictive learning architecture and objective that enables learning visual representations capable of solving a wide range of visual correspondence tasks in a zero-shot manner. Subsequently, I'll present a transformer-based approach for photorealistic video generation via diffusion modeling. Our approach jointly compresses images and videos within a unified latent space, enabling training and generation across modalities. Finally, I will illustrate the practical applications of generative models for robot learning. Our non-autoregressive, action-conditioned video generation model can act as a world model, enabling embodied agents to plan using visual model-predictive control. Furthermore, I'll showcase a generalist agent trained via next token prediction to learn from diverse robotic experiences across various robots and tasks.

Subject Added Entry-Topical Term  

Robots.

Subject Added Entry-Topical Term  

Success.

Subject Added Entry-Topical Term  

Failure analysis.

Subject Added Entry-Topical Term  

Video recordings.

Subject Added Entry-Topical Term  

Semantics.

Subject Added Entry-Topical Term  

Film studies.

Subject Added Entry-Topical Term  

Logic.

Subject Added Entry-Topical Term  

Robotics.

Added Entry-Corporate Name  

Stanford University.

Host Item Entry  

Dissertations Abstracts International. 86-04B.

Electronic Location and Access  

로그인을 한후 보실 수 있는 자료입니다.

Control Number  

joongbu:657555