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Scalable Lifelong Imitation Learning for Robot Fleets
Scalable Lifelong Imitation Learning for Robot Fleets
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151446
- ISBN
- 9798384452522
- DDC
- 004
- 저자명
- Hoque, Ryan.
- 서명/저자
- Scalable Lifelong Imitation Learning for Robot Fleets
- 발행사항
- [Sl] : University of California, Berkeley, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 196 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-03, Section: B.
- 주기사항
- Advisor: Goldberg, Ken.
- 학위논문주기
- Thesis (Ph.D.)--University of California, Berkeley, 2024.
- 초록/해제
- 요약Recent breakthroughs in deep learning have revolutionized natural language processing, computer vision, and robotics. Nevertheless, reliable robot autonomy in unstructured environments remains elusive. Without the Internet-scale data available for language and vision, robotics faces a unique chicken-and-egg problem: robot learning requires large datasets from deployment at scale, but robot learning is not yet reliable enough for deployment at scale. We propose a scalable human-in-the-loop learning paradigm as a potential solution to this paradox, and we argue that it is the key ingredient behind the recent growth of large-scale robot deployments in applications such as autonomous driving and e-commerce order fulfillment. We develop novel formalisms, algorithms, benchmarks, systems, and applications for this setting and evaluate its performance in extensive simulation and physical experiments. This dissertation is composed of three complementary parts. In Part I, we propose novel algorithms and systems for interactive imitation learning, in which autonomous robots can actively query human supervisors for assistance when needed. In Part II, we introduce interactive fleet learning, which generalizes interactive imitation learning to multiple robots and multiple human supervisors. In Part III, we introduce and study systems for remote supervision of robot fleets over the Internet, enabling interactive fleet learning at a distance. Throughout this thesis, we design algorithms and systems with an emphasis on scalability in terms of the number of robots, number of humans, amount of human supervision required, dataset size, and distribution of physical locations. We conclude with a discussion of limitations and opportunities for future work.
- 일반주제명
- Computer science
- 일반주제명
- Robotics
- 일반주제명
- Computer engineering
- 키워드
- Fleet learning
- 기타저자
- University of California, Berkeley Electrical Engineering & Computer Sciences
- 기본자료저록
- Dissertations Abstracts International. 86-03B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520250211151446
■006m o d
■007cr#unu||||||||
■020 ▼a9798384452522
■035 ▼a(MiAaPQ)AAI31296445
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a004
■1001 ▼aHoque, Ryan.
■24510▼aScalable Lifelong Imitation Learning for Robot Fleets
■260 ▼a[Sl]▼bUniversity of California, Berkeley▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a196 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-03, Section: B.
■500 ▼aAdvisor: Goldberg, Ken.
■5021 ▼aThesis (Ph.D.)--University of California, Berkeley, 2024.
■520 ▼aRecent breakthroughs in deep learning have revolutionized natural language processing, computer vision, and robotics. Nevertheless, reliable robot autonomy in unstructured environments remains elusive. Without the Internet-scale data available for language and vision, robotics faces a unique chicken-and-egg problem: robot learning requires large datasets from deployment at scale, but robot learning is not yet reliable enough for deployment at scale. We propose a scalable human-in-the-loop learning paradigm as a potential solution to this paradox, and we argue that it is the key ingredient behind the recent growth of large-scale robot deployments in applications such as autonomous driving and e-commerce order fulfillment. We develop novel formalisms, algorithms, benchmarks, systems, and applications for this setting and evaluate its performance in extensive simulation and physical experiments. This dissertation is composed of three complementary parts. In Part I, we propose novel algorithms and systems for interactive imitation learning, in which autonomous robots can actively query human supervisors for assistance when needed. In Part II, we introduce interactive fleet learning, which generalizes interactive imitation learning to multiple robots and multiple human supervisors. In Part III, we introduce and study systems for remote supervision of robot fleets over the Internet, enabling interactive fleet learning at a distance. Throughout this thesis, we design algorithms and systems with an emphasis on scalability in terms of the number of robots, number of humans, amount of human supervision required, dataset size, and distribution of physical locations. We conclude with a discussion of limitations and opportunities for future work.
■590 ▼aSchool code: 0028.
■650 4▼aComputer science
■650 4▼aRobotics
■650 4▼aComputer engineering
■653 ▼aFleet learning
■653 ▼aImitation learning
■653 ▼aRobot manipulation
■653 ▼aAutonomous driving
■653 ▼aHuman supervision
■690 ▼a0984
■690 ▼a0771
■690 ▼a0800
■690 ▼a0464
■71020▼aUniversity of California, Berkeley▼bElectrical Engineering & Computer Sciences.
■7730 ▼tDissertations Abstracts International▼g86-03B.
■790 ▼a0028
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17161792▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
