자료유형  

 학위논문

Control Number  

0017162576

International Standard Book Number  

9798383200384

Dewey Decimal Classification Number  

004

Main Entry-Personal Name  

Chi, Cheng.

Publication, Distribution, etc. (Imprint  

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

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2024

Physical Description  

131 p.

General Note  

Source: Dissertations Abstracts International, Volume: 86-01, Section: B.

General Note  

Advisor: Song, Shuran;Vondrick, Carl.

Dissertation Note  

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

Summary, Etc.  

요약Deploying robots in real-world environments involves immense engineering complexity, potentially surpassing the resources required for autonomous vehicles due to the increased dimensionality and task variety. To maximize the chances of successful real-world deployment, finding a simple solution that minimizes engineering complexity at every level, from hardware to algorithm to operations, is crucial. In this dissertation, we consider a vision-based manipulation system that can be deployed in-the-wild when trained to imitate sufficient quantity and diversity of human demonstration data on the desired task. At deployment time, the robot is driven by a single diffusion-based visuomotor policy, with raw RGB images as input and robot end-effector pose as output. Compared to existing policy representations, Diffusion Policy handles multimodal action distributions gracefully, being scalable to high-dimensional action spaces and exhibiting impressive training stability. These properties allow a single software system to be used for multiple tasks, with data collected by multiple demonstrators, deployed to multiple robot embodiments, and without significant hyperparameter tuning. We developed a Universal Manipulation Interface (UMI), a portable, low-cost, and information-rich data collection system to enable direct manipulation skill learning from in-the-wild human demonstrations. UMI provides an intuitive interface for non-expert users by using hand-held grippers with mounted GoPro cameras. Compared to existing robotic data collection systems, UMI enables robotic data collection without needing a robot, drastically reducing the engineering and operational complexity. Trained with UMI data, the resulting diffusion policies can be deployed across multiple robot platforms in unseen environments for novel objects and to complete dynamic, bimanual, precise, and long-horizon tasks.The Diffusion Policy and UMI combination provides a simple full-stack solution to many manipulation problems. The turn-around time of building a single-task manipulation system (such as object tossing and cloth folding) can be reduced from a few months to a few days.

Subject Added Entry-Topical Term  

Computer science.

Subject Added Entry-Topical Term  

Computer engineering.

Index Term-Uncontrolled  

Universal Manipulation Interface

Index Term-Uncontrolled  

Vision manipulation

Index Term-Uncontrolled  

Diffusion Policy

Added Entry-Corporate Name  

Columbia University Computer Science

Host Item Entry  

Dissertations Abstracts International. 86-01B.

Electronic Location and Access  

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Control Number  

joongbu:654033