{"id":{"repo_id":"umkc","oai_identifier":"oai:mospace.umsystem.edu:10355/110323"},"canonical_url":"https://search.dev.ndltd.org/etd/umkc/oai:mospace.umsystem.edu:10355/110323","repository":{"repo_id":"umkc","name":"University of Missouri - Kansas City","base_url":"https://mospace.umsystem.edu/oai/request"},"display":{"title":"Intelligent robotics with digital-twin alignment : semantic navigation, manipulation, planning, and human-to-robot action transformation","abstract":"This dissertation advances AI-empowered indoor robotics through four interconnected contributions that unify navigation, manipulation, semantic planning, and human-to-robot action transformation within a digital-twin-aligned framework. GRIP, a grid-aware semantic navigation module, integrates symbolic scene understanding with hybrid search-and-policy execution to achieve robust and context-aware ObjectNav. PathFormer, a transformer-based manipulation model structured around a 3D spatial--semantic grid, generates smooth, interpretable, and physically consistent trajectories that remain tightly aligned with digital-twin simulation. KG-Transformer, a knowledge-guided semantic planner, leverages a lightweight digital twin to calibrate execution, veto unsafe behaviors, and autonomously repair failing plans across diverse indoor environments. ActionFormer, an action-generation transformer, introduces a unified imitation-learning pipeline that integrates human-activity recognition, human-motion generation, and robot-motion generation. ActionFormer supports more than twenty complex human activities, producing robot-ready demonstrations that generalize across platforms and enable end-to-end imitation learning from video and landmark sequences. Collectively, these contributions establish a coherent foundation for AI-empowered robotics grounded in digital-twin intelligence. Across benchmarks and real-world deployments, GRIP yields up to 9.6% higher success rate and more than 2x gains in path efficiency (SPL, SAE). PathFormer produces digitally consistent manipulation trajectories validated through robust sim-to-real transfer. KG-Transformer achieves 99.6% executability, delivers a +4.6-point improvement on unseen-scene tasks, and eliminates safety violations in both simulated and multi-robot execution. ActionFormer attains state-of-the-art performance in human-activity recognition and high execution accuracy across more than 20 activities, generating realistic human-motion traces and corresponding robot-motion trajectories for embodied robotic demonstration. Together, these advances deliver a trustworthy, semantically aligned, and high-performance simulation-to-reality pipeline that significantly enhances the adaptability, reliability, and real-world readiness of autonomous indoor robotic systems.","abstract_html":"This dissertation advances AI-empowered indoor robotics through four interconnected contributions that unify navigation, manipulation, semantic planning, and human-to-robot action transformation within a digital-twin-aligned framework. GRIP, a grid-aware semantic navigation module, integrates symbolic scene understanding with hybrid search-and-policy execution to achieve robust and context-aware ObjectNav. PathFormer, a transformer-based manipulation model structured around a 3D spatial--semantic grid, generates smooth, interpretable, and physically consistent trajectories that remain tightly aligned with digital-twin simulation. KG-Transformer, a knowledge-guided semantic planner, leverages a lightweight digital twin to calibrate execution, veto unsafe behaviors, and autonomously repair failing plans across diverse indoor environments. ActionFormer, an action-generation transformer, introduces a unified imitation-learning pipeline that integrates human-activity recognition, human-motion generation, and robot-motion generation. ActionFormer supports more than twenty complex human activities, producing robot-ready demonstrations that generalize across platforms and enable end-to-end imitation learning from video and landmark sequences. Collectively, these contributions establish a coherent foundation for AI-empowered robotics grounded in digital-twin intelligence. Across benchmarks and real-world deployments, GRIP yields up to 9.6% higher success rate and more than 2x gains in path efficiency (SPL, SAE). PathFormer produces digitally consistent manipulation trajectories validated through robust sim-to-real transfer. KG-Transformer achieves 99.6% executability, delivers a +4.6-point improvement on unseen-scene tasks, and eliminates safety violations in both simulated and multi-robot execution. ActionFormer attains state-of-the-art performance in human-activity recognition and high execution accuracy across more than 20 activities, generating realistic human-motion traces and corresponding robot-motion trajectories for embodied robotic demonstration. Together, these advances deliver a trustworthy, semantically aligned, and high-performance simulation-to-reality pipeline that significantly enhances the adaptability, reliability, and real-world readiness of autonomous indoor robotic systems.","abstract_has_math":false,"creators":["Alanazi, Ahmed Hamdan"],"institution":"University of Missouri--Kansas City","degree_name":"Ph.D. (Doctor of Philosophy)","degree_level":"Doctoral","degree_discipline":"Computer Science (UMKC)","degree_department":null,"school":null,"contributors":[],"advisors":["Lee, Yugyung, 1960-"],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025","date_published":"2025","updated_at":"2026-07-24T05:16:20Z","subjects":[],"languages":["en_US"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10355/110323","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Lee, Yugyung, 1960-"]},{"key":"dc:creator","label":"Author","values":["Alanazi, Ahmed Hamdan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-01-26T17:02:35Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2026-01-26T17:02:35Z"]},{"key":"dc:date.issued","label":"Date","values":["2025"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science (UMKC)"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D. (Doctor of Philosophy)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Missouri--Kansas City"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en_US"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10355/110323"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Title from PDF of title page viewed February 5, 2026","Dissertation advisor: Yugyung Lee","Vita","Includes bibliographical references (pages 169-187)","Dissertation (Ph.D)--Department of Computer Science and Electrical Engineering. University of Missouri--Kansas City, 2025"]},{"key":"dc:description.abstract","label":"Abstract","values":["This dissertation advances AI-empowered indoor robotics through four interconnected contributions that unify navigation, manipulation, semantic planning, and human-to-robot action transformation within a digital-twin-aligned framework. GRIP, a grid-aware semantic navigation module, integrates symbolic scene understanding with hybrid search-and-policy execution to achieve robust and context-aware ObjectNav. PathFormer, a transformer-based manipulation model structured around a 3D spatial--semantic grid, generates smooth, interpretable, and physically consistent trajectories that remain tightly aligned with digital-twin simulation. KG-Transformer, a knowledge-guided semantic planner, leverages a lightweight digital twin to calibrate execution, veto unsafe behaviors, and autonomously repair failing plans across diverse indoor environments. ActionFormer, an action-generation transformer, introduces a unified imitation-learning pipeline that integrates human-activity recognition, human-motion generation, and robot-motion generation. ActionFormer supports more than twenty complex human activities, producing robot-ready demonstrations that generalize across platforms and enable end-to-end imitation learning from video and landmark sequences. Collectively, these contributions establish a coherent foundation for AI-empowered robotics grounded in digital-twin intelligence. Across benchmarks and real-world deployments, GRIP yields up to 9.6% higher success rate and more than 2x gains in path efficiency (SPL, SAE). PathFormer produces digitally consistent manipulation trajectories validated through robust sim-to-real transfer. KG-Transformer achieves 99.6% executability, delivers a +4.6-point improvement on unseen-scene tasks, and eliminates safety violations in both simulated and multi-robot execution. ActionFormer attains state-of-the-art performance in human-activity recognition and high execution accuracy across more than 20 activities, generating realistic human-motion traces and corresponding robot-motion trajectories for embodied robotic demonstration. Together, these advances deliver a trustworthy, semantically aligned, and high-performance simulation-to-reality pipeline that significantly enhances the adaptability, reliability, and real-world readiness of autonomous indoor robotic systems."]},{"key":"dc:title","label":"Title","values":["Intelligent robotics with digital-twin alignment : semantic navigation, manipulation, planning, and human-to-robot action transformation"]}]}],"canonical_facts":{"dc:contributor.advisor":["Lee, Yugyung, 1960-"],"dc:creator":["Alanazi, Ahmed Hamdan"],"dc:date.accessioned":["2026-01-26T17:02:35Z"],"dc:date.available":["2026-01-26T17:02:35Z"],"dc:date.issued":["2025"],"dc:description":["Title from PDF of title page viewed February 5, 2026","Dissertation advisor: Yugyung Lee","Vita","Includes bibliographical references (pages 169-187)","Dissertation (Ph.D)--Department of Computer Science and Electrical Engineering. University of Missouri--Kansas City, 2025"],"dc:description.abstract":["This dissertation advances AI-empowered indoor robotics through four interconnected contributions that unify navigation, manipulation, semantic planning, and human-to-robot action transformation within a digital-twin-aligned framework. GRIP, a grid-aware semantic navigation module, integrates symbolic scene understanding with hybrid search-and-policy execution to achieve robust and context-aware ObjectNav. PathFormer, a transformer-based manipulation model structured around a 3D spatial--semantic grid, generates smooth, interpretable, and physically consistent trajectories that remain tightly aligned with digital-twin simulation. KG-Transformer, a knowledge-guided semantic planner, leverages a lightweight digital twin to calibrate execution, veto unsafe behaviors, and autonomously repair failing plans across diverse indoor environments. ActionFormer, an action-generation transformer, introduces a unified imitation-learning pipeline that integrates human-activity recognition, human-motion generation, and robot-motion generation. ActionFormer supports more than twenty complex human activities, producing robot-ready demonstrations that generalize across platforms and enable end-to-end imitation learning from video and landmark sequences. Collectively, these contributions establish a coherent foundation for AI-empowered robotics grounded in digital-twin intelligence. Across benchmarks and real-world deployments, GRIP yields up to 9.6% higher success rate and more than 2x gains in path efficiency (SPL, SAE). PathFormer produces digitally consistent manipulation trajectories validated through robust sim-to-real transfer. KG-Transformer achieves 99.6% executability, delivers a +4.6-point improvement on unseen-scene tasks, and eliminates safety violations in both simulated and multi-robot execution. ActionFormer attains state-of-the-art performance in human-activity recognition and high execution accuracy across more than 20 activities, generating realistic human-motion traces and corresponding robot-motion trajectories for embodied robotic demonstration. Together, these advances deliver a trustworthy, semantically aligned, and high-performance simulation-to-reality pipeline that significantly enhances the adaptability, reliability, and real-world readiness of autonomous indoor robotic systems."],"dc:identifier.uri":["https://hdl.handle.net/10355/110323"],"dc:language.iso":["en_US"],"dc:title":["Intelligent robotics with digital-twin alignment : semantic navigation, manipulation, planning, and human-to-robot action transformation"],"dc:type":["Thesis"],"thesis:degree_discipline":["Computer Science (UMKC)"],"thesis:degree_level":["Doctoral"],"thesis:degree_name":["Ph.D. (Doctor of Philosophy)"],"thesis:institution_name":["University of Missouri--Kansas City"]},"updated_at":"2026-07-24T05:16:20Z"}