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How to Train Your Robot: Techniques for Enabling Robotic Learning in the Real World- [electronic resource]
How to Train Your Robot: Techniques for Enabling Robotic Learning in the Real World- [electronic resource]
상세정보
- 자료유형
- 학위논문파일 국외
- 최종처리일시
- 20240214095853
- ISBN
- 9798380620116
- DDC
- 004
- 저자명
- Gupta, Abhishek.
- 서명/저자
- How to Train Your Robot: Techniques for Enabling Robotic Learning in the Real World - [electronic resource]
- 발행사항
- [S.l.]: : University of California, Berkeley., 2021
- 발행사항
- Ann Arbor : : ProQuest Dissertations & Theses,, 2021
- 형태사항
- 1 online resource(303 p.)
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-04, Section: B.
- 주기사항
- Advisor: Levine, Sergey;Abbeel, Pieter.
- 학위논문주기
- Thesis (Ph.D.)--University of California, Berkeley, 2021.
- 사용제한주기
- This item must not be sold to any third party vendors.
- 초록/해제
- 요약Reinforcement learning has been a powerful tool for building continuously improving systems in domains like video games and animated character control, but has proven relatively more challenging to apply to problems in real world robotics. In this talk, I will argue that this challenge can be attributed to a mismatch in assumptions between typical RL algorithms and what the real world actually provides, making data collection and utilization difficult. In this talk, I will discuss how to build algorithms and systems to bridge these assumptions and allow robotic learning systems to operate under the assumptions of the real world - under realistic and practical assumptions on non-determinism, uncertainty and human supervision. In particular, I will describe how we can develop algorithms to ensure easily scalable supervision from humans, perform safe, directed exploration in practical time scales and enable uninterrupted autonomous data collection at scale. I will show how these techniques can be applied to real world robotic systems. Lastly, I will provide some perspectives on how this opens the door towards future deployment of robots into unstructured human-centric environments such as our homes, hospitals, and shopping malls.
- 일반주제명
- Computer science.
- 일반주제명
- Robotics.
- 키워드
- Machine learning
- 키워드
- Video games
- 기타저자
- University of California, Berkeley Electrical Engineering & Computer Sciences
- 기본자료저록
- Dissertations Abstracts International. 85-04B.
- 기본자료저록
- Dissertation Abstract International
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520240214095853
■006m o d
■007cr#unu||||||||
■020 ▼a9798380620116
■035 ▼a(MiAaPQ)AAI28717829
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a004
■1001 ▼aGupta, Abhishek.
■24510▼aHow to Train Your Robot: Techniques for Enabling Robotic Learning in the Real World▼h[electronic resource]
■260 ▼a[S.l.]:▼bUniversity of California, Berkeley. ▼c2021
■260 1▼aAnn Arbor :▼bProQuest Dissertations & Theses, ▼c2021
■300 ▼a1 online resource(303 p.)
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-04, Section: B.
■500 ▼aAdvisor: Levine, Sergey;Abbeel, Pieter.
■5021 ▼aThesis (Ph.D.)--University of California, Berkeley, 2021.
■506 ▼aThis item must not be sold to any third party vendors.
■520 ▼aReinforcement learning has been a powerful tool for building continuously improving systems in domains like video games and animated character control, but has proven relatively more challenging to apply to problems in real world robotics. In this talk, I will argue that this challenge can be attributed to a mismatch in assumptions between typical RL algorithms and what the real world actually provides, making data collection and utilization difficult. In this talk, I will discuss how to build algorithms and systems to bridge these assumptions and allow robotic learning systems to operate under the assumptions of the real world - under realistic and practical assumptions on non-determinism, uncertainty and human supervision. In particular, I will describe how we can develop algorithms to ensure easily scalable supervision from humans, perform safe, directed exploration in practical time scales and enable uninterrupted autonomous data collection at scale. I will show how these techniques can be applied to real world robotic systems. Lastly, I will provide some perspectives on how this opens the door towards future deployment of robots into unstructured human-centric environments such as our homes, hospitals, and shopping malls.
■590 ▼aSchool code: 0028.
■650 4▼aComputer science.
■650 4▼aRobotics.
■653 ▼aMachine learning
■653 ▼aReinforcement learning
■653 ▼aVideo games
■690 ▼a0984
■690 ▼a0800
■690 ▼a0771
■71020▼aUniversity of California, Berkeley▼bElectrical Engineering & Computer Sciences.
■7730 ▼tDissertations Abstracts International▼g85-04B.
■773 ▼tDissertation Abstract International
■790 ▼a0028
■791 ▼aPh.D.
■792 ▼a2021
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T16931012▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
■980 ▼a202402▼f2024
