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Task-Driven Perception and Control for Robust and Efficient Autonomy
Task-Driven Perception and Control for Robust and Efficient Autonomy
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151416
- ISBN
- 9798382806754
- DDC
- 629.8
- 서명/저자
- Task-Driven Perception and Control for Robust and Efficient Autonomy
- 발행사항
- [Sl] : Princeton University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 204 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
- 주기사항
- Advisor: Majumdar, Anirudha.
- 학위논문주기
- Thesis (Ph.D.)--Princeton University, 2024.
- 초록/해제
- 요약Modern robotic applications have been propelled by exciting advancements in rich sensor technologies such as LiDAR and RGB-D cameras. However, when we deploy our robots in real environments using these sensors, we see them collide with objects or perform unexpected actions. These failures are often occurring for two reasons: (i) the high-dimensional sensors provide rich information that lead to the downstream control policy being sensitive to task-irrelevant distractors in the environment (e.g., changes in lighting conditions), and (ii) we deploy the robots without formal safety and performance assurances, specifically assurances that account for perception uncertainty, for operating in new environments.We break this dissertation into three parts to address these challenges. We first advocate for a task-driven perception and control design paradigm that aims to find minimalistic representations of the environment that are sufficient for the robot to complete its given task. We demonstrate in the first two parts that such a design paradigm affords robustness to task-irrelevant distractors in the environment and computational efficiency for the robot's control policy. In particular, we explore a memory-based and an attention-based perspective to this design paradigm. In the concluding part, we examine the importance of deploying robots with safety and performance assurances and demonstrate that formal assurances help drive empirical improvements to safety (e.g., reduced number of collisions in new environments). Throughout all of the parts, a central focus revolves around enhancing robot performance for real settings. As such, we showcase the majority of our proposed methodologies through various tracking and navigation tasks on a physical quadruped robot using either LiDAR or RGB-D cameras.
- 일반주제명
- Robotics
- 일반주제명
- Aerospace engineering
- 일반주제명
- Mechanical engineering
- 키워드
- Attention
- 키워드
- Biostimulation
- 키워드
- Robot memory
- 기타저자
- Princeton University Mechanical and Aerospace Engineering
- 기본자료저록
- Dissertations Abstracts International. 85-12B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■006m o d
■007cr#unu||||||||
■020 ▼a9798382806754
■035 ▼a(MiAaPQ)AAI31293551
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a629.8
■1001 ▼aBooker, Meghan Elizabeth.
■24510▼aTask-Driven Perception and Control for Robust and Efficient Autonomy
■260 ▼a[Sl]▼bPrinceton University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a204 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-12, Section: B.
■500 ▼aAdvisor: Majumdar, Anirudha.
■5021 ▼aThesis (Ph.D.)--Princeton University, 2024.
■520 ▼aModern robotic applications have been propelled by exciting advancements in rich sensor technologies such as LiDAR and RGB-D cameras. However, when we deploy our robots in real environments using these sensors, we see them collide with objects or perform unexpected actions. These failures are often occurring for two reasons: (i) the high-dimensional sensors provide rich information that lead to the downstream control policy being sensitive to task-irrelevant distractors in the environment (e.g., changes in lighting conditions), and (ii) we deploy the robots without formal safety and performance assurances, specifically assurances that account for perception uncertainty, for operating in new environments.We break this dissertation into three parts to address these challenges. We first advocate for a task-driven perception and control design paradigm that aims to find minimalistic representations of the environment that are sufficient for the robot to complete its given task. We demonstrate in the first two parts that such a design paradigm affords robustness to task-irrelevant distractors in the environment and computational efficiency for the robot's control policy. In particular, we explore a memory-based and an attention-based perspective to this design paradigm. In the concluding part, we examine the importance of deploying robots with safety and performance assurances and demonstrate that formal assurances help drive empirical improvements to safety (e.g., reduced number of collisions in new environments). Throughout all of the parts, a central focus revolves around enhancing robot performance for real settings. As such, we showcase the majority of our proposed methodologies through various tracking and navigation tasks on a physical quadruped robot using either LiDAR or RGB-D cameras.
■590 ▼aSchool code: 0181.
■650 4▼aRobotics
■650 4▼aAerospace engineering
■650 4▼aMechanical engineering
■653 ▼aAttention
■653 ▼aBiostimulation
■653 ▼aReinforcement learning
■653 ▼aRobot memory
■653 ▼aModern robotic applications
■690 ▼a0771
■690 ▼a0548
■690 ▼a0538
■71020▼aPrinceton University▼bMechanical and Aerospace Engineering.
■7730 ▼tDissertations Abstracts International▼g85-12B.
■790 ▼a0181
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17161584▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
