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Learning Low-Dimensional Latent Representations of Demonstrated Trajectories for Robots
Learning Low-Dimensional Latent Representations of Demonstrated Trajectories for Robots
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211152023
- ISBN
- 9798384049487
- DDC
- 629.8
- 저자명
- Rhodes, Travers.
- 서명/저자
- Learning Low-Dimensional Latent Representations of Demonstrated Trajectories for Robots
- 발행사항
- [Sl] : Cornell University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 155 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-03, Section: B.
- 주기사항
- Advisor: Lee, Daniel.
- 학위논문주기
- Thesis (Ph.D.)--Cornell University, 2024.
- 초록/해제
- 요약For robots to perform intricate manipulation skills, like picking up a slippery banana slice with a fork, it is often useful to have a human demonstrate how to perform that skill for the robot. Humans can perform the desired motion multiple times in front of the robot, and the robot can record the demonstrated trajectories and build a model of the demonstrations. If the robot can learn a good model of the different ways to perform the desired motion, the human and the robot can then work together to pick a trajectory for the robot to perform to solve the task. This dissertation investigates the machine learning component of that example: "How can a robot learn a good model of demonstrated trajectories?" We present multiple advances in the ability of robots to model demonstrated trajectories using latent variable models. These approaches include better model regularization to take advantage of the small size of datasets of human demonstrations, better architectural choices to separate the timing and spatial variations of the demonstrated trajectories, and an investigation into how to disentangle the meaning of the variables in the latent variable model. Theoretical justifications for the contributions are presented alongside empirical evaluations performed on a physical robot arm.
- 일반주제명
- Robotics
- 일반주제명
- Computer science
- 키워드
- Manipulation
- 기타저자
- Cornell University Computer Science
- 기본자료저록
- Dissertations Abstracts International. 86-03B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520250211152023
■006m o d
■007cr#unu||||||||
■020 ▼a9798384049487
■035 ▼a(MiAaPQ)AAI31332636
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a629.8
■1001 ▼aRhodes, Travers.▼0(orcid)0000-0002-2142-5388
■24510▼aLearning Low-Dimensional Latent Representations of Demonstrated Trajectories for Robots
■260 ▼a[Sl]▼bCornell University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a155 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-03, Section: B.
■500 ▼aAdvisor: Lee, Daniel.
■5021 ▼aThesis (Ph.D.)--Cornell University, 2024.
■520 ▼aFor robots to perform intricate manipulation skills, like picking up a slippery banana slice with a fork, it is often useful to have a human demonstrate how to perform that skill for the robot. Humans can perform the desired motion multiple times in front of the robot, and the robot can record the demonstrated trajectories and build a model of the demonstrations. If the robot can learn a good model of the different ways to perform the desired motion, the human and the robot can then work together to pick a trajectory for the robot to perform to solve the task. This dissertation investigates the machine learning component of that example: "How can a robot learn a good model of demonstrated trajectories?" We present multiple advances in the ability of robots to model demonstrated trajectories using latent variable models. These approaches include better model regularization to take advantage of the small size of datasets of human demonstrations, better architectural choices to separate the timing and spatial variations of the demonstrated trajectories, and an investigation into how to disentangle the meaning of the variables in the latent variable model. Theoretical justifications for the contributions are presented alongside empirical evaluations performed on a physical robot arm.
■590 ▼aSchool code: 0058.
■650 4▼aRobotics
■650 4▼aComputer science
■653 ▼aGenerative models
■653 ▼aLearning from demonstration
■653 ▼aManipulation
■653 ▼aUnsupervised learning
■690 ▼a0771
■690 ▼a0800
■690 ▼a0984
■71020▼aCornell University▼bComputer Science.
■7730 ▼tDissertations Abstracts International▼g86-03B.
■790 ▼a0058
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17162533▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
