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Enabling Efficient and Reliable Robot Manipulation Through Optimization, Interactive Perception and Self-Supervised Learning
Enabling Efficient and Reliable Robot Manipulation Through Optimization, Interactive Perception and Self-Supervised Learning
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151449
- ISBN
- 9798384449997
- DDC
- 620
- 저자명
- Avigal, Yahav.
- 서명/저자
- Enabling Efficient and Reliable Robot Manipulation Through Optimization, Interactive Perception and Self-Supervised Learning
- 발행사항
- [Sl] : University of California, Berkeley, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 249 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-04, Section: B.
- 주기사항
- Advisor: Goldberg, Ken.
- 학위논문주기
- Thesis (Ph.D.)--University of California, Berkeley, 2024.
- 초록/해제
- 요약Robot manipulation research is essential for advancing automation technologies, allowing robotic arms to execute complex and precise tasks across various industries. In recent years, several technologies have matured and transformed industries, such as logistics and manufacturing automation, through advancements like robot grasping. To enable the adoption of these emerging capabilities into real-world automation scenarios, these technologies need to be both efficient and reliable. However, achieving a balance between efficiency and reliability is challenging, as improving one often requires compromising the other. As a result, many impressive methods, initially developed for practical applications, struggle to make the transition into industry use. This thesis explores five research areas within the field of robot manipulation, examining diverse angles and domains such as industrial automation, deformable manipulation, agricultural robotics, and surgical robotics. It proposes strategies to achieve efficient and reliable robot manipulation. By focusing on enhancing both efficiency and reliability, the thesis aims to facilitate the transition of robot manipulation from proof of concept to practical applications. Among the various methods explored, three primary strategies have proven to be particularly effective: constrained optimization, which given a reliable model, applies strict mathematical constraints to find efficient and reliable solutions; interactive perception and self-supervised learning, which are used to improve the efficiency and reliability in situations where there is high uncertainty in the dynamics of the system or a lack a reliable model. The thesis concludes by discussing the key insights gained through this research, reviewing lessons learned, and suggesting potential directions for future research.
- 일반주제명
- Engineering
- 일반주제명
- Computer engineering
- 일반주제명
- Computer science
- 키워드
- Reliability
- 기타저자
- University of California, Berkeley Electrical Engineering & Computer Sciences
- 기본자료저록
- Dissertations Abstracts International. 86-04B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■006m o d
■007cr#unu||||||||
■020 ▼a9798384449997
■035 ▼a(MiAaPQ)AAI31296667
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a620
■1001 ▼aAvigal, Yahav.
■24510▼aEnabling Efficient and Reliable Robot Manipulation Through Optimization, Interactive Perception and Self-Supervised Learning
■260 ▼a[Sl]▼bUniversity of California, Berkeley▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a249 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-04, Section: B.
■500 ▼aAdvisor: Goldberg, Ken.
■5021 ▼aThesis (Ph.D.)--University of California, Berkeley, 2024.
■520 ▼aRobot manipulation research is essential for advancing automation technologies, allowing robotic arms to execute complex and precise tasks across various industries. In recent years, several technologies have matured and transformed industries, such as logistics and manufacturing automation, through advancements like robot grasping. To enable the adoption of these emerging capabilities into real-world automation scenarios, these technologies need to be both efficient and reliable. However, achieving a balance between efficiency and reliability is challenging, as improving one often requires compromising the other. As a result, many impressive methods, initially developed for practical applications, struggle to make the transition into industry use. This thesis explores five research areas within the field of robot manipulation, examining diverse angles and domains such as industrial automation, deformable manipulation, agricultural robotics, and surgical robotics. It proposes strategies to achieve efficient and reliable robot manipulation. By focusing on enhancing both efficiency and reliability, the thesis aims to facilitate the transition of robot manipulation from proof of concept to practical applications. Among the various methods explored, three primary strategies have proven to be particularly effective: constrained optimization, which given a reliable model, applies strict mathematical constraints to find efficient and reliable solutions; interactive perception and self-supervised learning, which are used to improve the efficiency and reliability in situations where there is high uncertainty in the dynamics of the system or a lack a reliable model. The thesis concludes by discussing the key insights gained through this research, reviewing lessons learned, and suggesting potential directions for future research.
■590 ▼aSchool code: 0028.
■650 4▼aEngineering
■650 4▼aComputer engineering
■650 4▼aComputer science
■653 ▼aRobot manipulation
■653 ▼aSelf-supervised learning
■653 ▼aConstrained optimization
■653 ▼aInteractive perception
■653 ▼aReliability
■690 ▼a0537
■690 ▼a0464
■690 ▼a0984
■690 ▼a0800
■71020▼aUniversity of California, Berkeley▼bElectrical Engineering & Computer Sciences.
■7730 ▼tDissertations Abstracts International▼g86-04B.
■790 ▼a0028
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17161819▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
