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Contact-Based Perception and Planning for Robotic Manipulation in Novel Environments
Contact-Based Perception and Planning for Robotic Manipulation in Novel Environments
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211152956
- ISBN
- 9798384043171
- DDC
- 004
- 저자명
- Zhong, Sheng.
- 서명/저자
- Contact-Based Perception and Planning for Robotic Manipulation in Novel Environments
- 발행사항
- [Sl] : University of Michigan, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 129 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-03, Section: B.
- 주기사항
- Advisor: Berenson, Dmitry.
- 학위논문주기
- Thesis (Ph.D.)--University of Michigan, 2024.
- 초록/해제
- 요약This thesis proposes a framework for autonomous robotic perception and planning for manipulation tasks in unknown environments by leveraging information from purposeful contacts and explicitly reasoning about uncertainty. The high-level goal is to enhance the amount of information that can be extracted from contacts, enabling greater utilization of this sensing modality. We focus on challenging tasks where objects to be manipulated are occluded by the environment, other objects, or themselves, which limits the applicability of purely visual sensing and necessitates contact-based information gathering.Each chapter of our work tackles a specific challenge arising from collecting information through contact, with the goal of enabling robots to explore autonomously. The first challenge we considered is the limited applicability of long-horizon planning when global perception is lacking. Traps may arise where the state remains in a cycle without accomplishing the goal, and we develop a hierarchical control scheme to detect and escape from traps.Contact-based exploration is also challenging due to the ambiguity of associating contact points to specific objects in multi-object environments. To resolve this, we present a method that maintains a belief over both current and past contact points without relying on rigid associations. This flexibility allows for the correction of erroneous estimates.Building on these contact point estimates, we infer the plausible poses of known objects. A key component in our method is the use of negative information-data indicating observed free space-which constrains possible object poses by measuring the discrepancy between these potential poses and the observed point clouds. This approach is especially effective in highly-occluded environments where visual object segmentation often fails.To integrate our pose estimates into real-time decision-making, we formulate a conditional probability on object poses given the disparity with observed point clouds. We derive a cost function from the mutual information between the object's pose and the occupancy of the workspace points, facilitating its application in closed-loop model predictive control (MPC). Our method also includes a reachability cost function to prevent objects from being pushed out of the robot's workspace and incorporates a stochastic dynamics model to predict information gain changes as the object is manipulated.The algorithms developed in this thesis emphasize efficient parallel computation and are evaluated using both simulated and real experiments. All implementations are made publicly available as open-source libraries.
- 일반주제명
- Computer science
- 일반주제명
- Robotics
- 키워드
- Contact sensing
- 기타저자
- University of Michigan Robotics
- 기본자료저록
- Dissertations Abstracts International. 86-03B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798384043171
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■035 ▼a(MiAaPQ)umichrackham005867
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a004
■1001 ▼aZhong, Sheng.
■24510▼aContact-Based Perception and Planning for Robotic Manipulation in Novel Environments
■260 ▼a[Sl]▼bUniversity of Michigan▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a129 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-03, Section: B.
■500 ▼aAdvisor: Berenson, Dmitry.
■5021 ▼aThesis (Ph.D.)--University of Michigan, 2024.
■520 ▼aThis thesis proposes a framework for autonomous robotic perception and planning for manipulation tasks in unknown environments by leveraging information from purposeful contacts and explicitly reasoning about uncertainty. The high-level goal is to enhance the amount of information that can be extracted from contacts, enabling greater utilization of this sensing modality. We focus on challenging tasks where objects to be manipulated are occluded by the environment, other objects, or themselves, which limits the applicability of purely visual sensing and necessitates contact-based information gathering.Each chapter of our work tackles a specific challenge arising from collecting information through contact, with the goal of enabling robots to explore autonomously. The first challenge we considered is the limited applicability of long-horizon planning when global perception is lacking. Traps may arise where the state remains in a cycle without accomplishing the goal, and we develop a hierarchical control scheme to detect and escape from traps.Contact-based exploration is also challenging due to the ambiguity of associating contact points to specific objects in multi-object environments. To resolve this, we present a method that maintains a belief over both current and past contact points without relying on rigid associations. This flexibility allows for the correction of erroneous estimates.Building on these contact point estimates, we infer the plausible poses of known objects. A key component in our method is the use of negative information-data indicating observed free space-which constrains possible object poses by measuring the discrepancy between these potential poses and the observed point clouds. This approach is especially effective in highly-occluded environments where visual object segmentation often fails.To integrate our pose estimates into real-time decision-making, we formulate a conditional probability on object poses given the disparity with observed point clouds. We derive a cost function from the mutual information between the object's pose and the occupancy of the workspace points, facilitating its application in closed-loop model predictive control (MPC). Our method also includes a reachability cost function to prevent objects from being pushed out of the robot's workspace and incorporates a stochastic dynamics model to predict information gain changes as the object is manipulated.The algorithms developed in this thesis emphasize efficient parallel computation and are evaluated using both simulated and real experiments. All implementations are made publicly available as open-source libraries.
■590 ▼aSchool code: 0127.
■650 4▼aComputer science
■650 4▼aRobotics
■653 ▼aRobotic manipulation
■653 ▼aInteractive perception
■653 ▼aModel predictive control
■653 ▼aUncertainty reasoning
■653 ▼aContact sensing
■690 ▼a0771
■690 ▼a0984
■690 ▼a0800
■71020▼aUniversity of Michigan▼bRobotics.
■7730 ▼tDissertations Abstracts International▼g86-03B.
■790 ▼a0127
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17164393▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
