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Grounded Semantic Reasoning for Robotic Interaction with Real-World Objects
Grounded Semantic Reasoning for Robotic Interaction with Real-World Objects
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202105538
- ISBN
- 9798263396022
- DDC
- 620
- 저자명
- Liu, Weiyu.
- 서명/저자
- Grounded Semantic Reasoning for Robotic Interaction with Real-World Objects
- 발행사항
- [Sl] : Georgia Institute of Technology, 2022
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2022
- 형태사항
- 256 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-05, Section: B.
- 주기사항
- Advisor: Chernova, Sonia.
- 학위논문주기
- Thesis (Ph.D.)--Georgia Institute of Technology, 2022.
- 초록/해제
- 요약Robots are increasingly transitioning from specialized, single-task machines to generalpurpose systems that operate in unstructured environments, such as homes, offices, and warehouses. In these real-world domains, robots need to manipulate novel objects while adapting to changes in environments and goals. Semantic knowledge, which concisely describes target domains with symbols, can potentially reveal the meaningful patterns shared between problems and environments. However, existing robots are yet to effectively reason about semantic data encoding complex relational knowledge or jointly reason about symbolic semantic data and multimodal data pertinent to robotic manipulation (e.g., object point clouds, 6-DoF poses, and attributes detected with multimodal sensing).This dissertation develops semantic reasoning frameworks capable of modeling complex semantic knowledge grounded in robot perception and action. We show that grounded semantic reasoning enables robots to more effectively perceive, model, and interact with objects in real-world environments. Specifically, this dissertation makes the following contributions: (1) a survey providing a unified view for the diversity of works in the field by formulating semantic reasoning as the integration of knowledge sources, computational frameworks, and world representations; (2) a method for predicting missing relations in large-scale knowledge graphs by leveraging type hierarchies of entities, effectively avoiding ambiguity while maintaining generalization of multi-hop reasoning patterns; (3) a method for predicting unknown properties of objects in various environmental contexts, outperforming prior knowledge graph and statistical relational learning methods due to the use of n-ary relations for modeling object properties; (4) a method for purposeful robotic grasping that accounts for a broad range of contexts (including object visual affordance, material, state, and task constraint), outperforming existing approaches in novel contexts and for unknown objects; (5) a systematic investigation into the generalization of task-oriented grasping that includes a benchmark dataset of 250k grasps, and a novel graph neural network that in corporates semantic relations into end-to-end learning of 6-DoF grasps; (6) a method for rearranging novel objects into semantically meaningful spatial structures based on highlevel language instructions, more effectively capturing multi-object spatial constraints than existing pairwise spatial representations; (7) a novel planning-inspired approach that iteratively optimizes placements of partially observed objects subject to both physical constraints and semantic constraints inferred from language instructions.
- 일반주제명
- Robots
- 일반주제명
- Neural networks
- 일반주제명
- Semantics
- 일반주제명
- Robotics
- 기본자료저록
- Dissertations Abstracts International. 87-05B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798263396022
■035 ▼a(MiAaPQ)AAI32314955
■035 ▼a(MiAaPQ)GeorgiaTech70187
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a620
■1001 ▼aLiu, Weiyu.
■24510▼aGrounded Semantic Reasoning for Robotic Interaction with Real-World Objects
■260 ▼a[Sl]▼bGeorgia Institute of Technology▼c2022
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2022
■300 ▼a256 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-05, Section: B.
■500 ▼aAdvisor: Chernova, Sonia.
■5021 ▼aThesis (Ph.D.)--Georgia Institute of Technology, 2022.
■520 ▼aRobots are increasingly transitioning from specialized, single-task machines to generalpurpose systems that operate in unstructured environments, such as homes, offices, and warehouses. In these real-world domains, robots need to manipulate novel objects while adapting to changes in environments and goals. Semantic knowledge, which concisely describes target domains with symbols, can potentially reveal the meaningful patterns shared between problems and environments. However, existing robots are yet to effectively reason about semantic data encoding complex relational knowledge or jointly reason about symbolic semantic data and multimodal data pertinent to robotic manipulation (e.g., object point clouds, 6-DoF poses, and attributes detected with multimodal sensing).This dissertation develops semantic reasoning frameworks capable of modeling complex semantic knowledge grounded in robot perception and action. We show that grounded semantic reasoning enables robots to more effectively perceive, model, and interact with objects in real-world environments. Specifically, this dissertation makes the following contributions: (1) a survey providing a unified view for the diversity of works in the field by formulating semantic reasoning as the integration of knowledge sources, computational frameworks, and world representations; (2) a method for predicting missing relations in large-scale knowledge graphs by leveraging type hierarchies of entities, effectively avoiding ambiguity while maintaining generalization of multi-hop reasoning patterns; (3) a method for predicting unknown properties of objects in various environmental contexts, outperforming prior knowledge graph and statistical relational learning methods due to the use of n-ary relations for modeling object properties; (4) a method for purposeful robotic grasping that accounts for a broad range of contexts (including object visual affordance, material, state, and task constraint), outperforming existing approaches in novel contexts and for unknown objects; (5) a systematic investigation into the generalization of task-oriented grasping that includes a benchmark dataset of 250k grasps, and a novel graph neural network that in corporates semantic relations into end-to-end learning of 6-DoF grasps; (6) a method for rearranging novel objects into semantically meaningful spatial structures based on highlevel language instructions, more effectively capturing multi-object spatial constraints than existing pairwise spatial representations; (7) a novel planning-inspired approach that iteratively optimizes placements of partially observed objects subject to both physical constraints and semantic constraints inferred from language instructions.
■590 ▼aSchool code: 0078.
■650 4▼aRobots
■650 4▼aNeural networks
■650 4▼aSemantics
■650 4▼aRobotics
■690 ▼a0800
■690 ▼a0771
■71020▼aGeorgia Institute of Technology.
■7730 ▼tDissertations Abstracts International▼g87-05B.
■790 ▼a0078
■791 ▼aPh.D.
■792 ▼a2022
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17360508▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
