자료유형  

 학위논문

Control Number  

0017163655

International Standard Book Number  

9798383705537

Dewey Decimal Classification Number  

629.8

Main Entry-Personal Name  

Wei, Tianhao.

Publication, Distribution, etc. (Imprint  

[S.l.] : Carnegie Mellon University., 2024

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2024

Physical Description  

218 p.

General Note  

Source: Dissertations Abstracts International, Volume: 86-02, Section: B.

General Note  

Advisor: Liu, Changliu.

Dissertation Note  

Thesis (Ph.D.)--Carnegie Mellon University, 2024.

Summary, Etc.  

요약Robots are increasingly deployed across various domains, from industrial automation to domestic assistance. Ensuring that robots operate safely and intelligently is crucial to preventing potential risks such as injury, loss of life, and economic costs. This thesis addresses key challenges in deploying robots in complex real-world environments, including providing formal safety guarantees in uncertain conditions, scaling safety guarantees to realistic high-dimensional systems, allowing the robot to behave intelligently while remaining explainable and trustworthy, and ensuring the robustness of neural network components.This thesis introduces a suite of tools to tackle these challenges. The first tool, Meta-Control, synthesizes heterogeneous robot skills with a hiearchical control approach, which could decompose system-level safety requirements into module-level constraints. These constraints are categorized into control and neural network constraints. For control constraints, the toolset introduces Abstract Safe Control for hierarchical safety guarantees, Robust Safe Control for handling model uncertainty through a control-limits aware robust framework, Neural Network Dynamic Models (NNDM) Safe Control for integrating data-driven models with safety guarantees, and Benchmark of Interactive Safety for benchmarking and unifying different safe control algorithms. For neural network constraints, the toolset introduces ModelVerification.jl toolbox for verifying neural network safety specifications, online verification for online assurance under domain shifts and network update, and the Signal-to-Noise Ratio (SNR) loss method to enhance stability and robustness of neural networks.These tools enable the provision of formal safety guarantees with partially known or unknown dynamic models in uncertain, interactive environments, achieving state-of-the-art control safety and neural network safety. This allows robot arms to perform various tasks efficiently and safely, advancing the development of reliable and trustworthy general-purpose robots.

Subject Added Entry-Topical Term  

Robotics.

Subject Added Entry-Topical Term  

Computer science.

Subject Added Entry-Topical Term  

Information technology.

Index Term-Uncontrolled  

Explainable AI

Index Term-Uncontrolled  

Neural network verification

Index Term-Uncontrolled  

Robot safety

Index Term-Uncontrolled  

Robust Safe Control

Index Term-Uncontrolled  

Network Dynamic Models

Added Entry-Corporate Name  

Carnegie Mellon University Electrical and Computer Engineering

Host Item Entry  

Dissertations Abstracts International. 86-02B.

Electronic Location and Access  

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Control Number  

joongbu:658425