{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/129326"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/129326","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Learning structured models for robotic manipulation of deformable objects","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","abstract_has_math":false,"creators":["Li, Baoyu"],"institution":"University of Illinois Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Hauser, Kris"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-05-06","date_published":"2025-05-06","updated_at":"2026-07-22T22:25:04Z","subjects":["Robot Learning","Robotic Manipulation","Deformable Object Manipulation","Graph-Based Neural Dynamics"],"languages":["en","eng"],"rights":["Copyright 2025 Baoyu Li"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/129326","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Hauser, Kris"]},{"key":"dc:creator","label":"Author","values":["Li, Baoyu"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-05-06","2025-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Robot Learning","Robotic Manipulation","Deformable Object Manipulation","Graph-Based Neural Dynamics"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2025 Baoyu Li"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/129326"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","The student, Baoyu Li, accepted the attached license on 2025-05-05 at 15:01.","The student, Baoyu Li, submitted this Thesis for approval on 2025-05-05 at 15:16.","This Thesis was approved for publication on 2025-05-06 at 16:18.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22215 on 2025-10-19 at 18:12:58","Robotic manipulation of deformable objects poses significant challenges due to their complex, nonlinear behaviors, high degrees of freedom, and variable material properties. Traditional model-based techniques are often inadequate at representing these subtleties in the real world, while many learning-based strategies struggle to generalize across different object shapes, poses, and environmental conditions. This thesis presents two novel and unified frameworks that address these challenges through the structured and adaptive modeling of diverse deformable objects. The first framework, Material-Adaptive Graph-Based Neural Dynamics (AdaptiGraph), uses particle-based representations enriched with material type information and continuous physical property parameters. By incorporating these features into a graph neural network and applying a test-time few-shot adaptation strategy, AdaptiGraph delivers precise dynamics predictions and facilitates online estimation of physical properties across various rigid and soft materials. Second, a Particle-Grid Neural Dynamics framework is proposed to directly learn the dynamics of deformable objects from depth images. This approach leverages the innate geometric and physical structure of particle-grid representations to capture fine-scale features, thereby providing accurate predictions of how deformable objects evolve during robotic interactions while remaining robust even under partial observation conditions. Extensive experiments in real-world robotics settings have demonstrated that these frameworks considerably improve manipulation performance in tasks involving 1D, 2D, and 3D deformable objects, including rope straightening, granular material gathering, cloth manipulation, and plush toy relocation. Together, these contributions advance the development of data-efficient robotic systems capable of operating in unstructured environments with diverse deformable materials, while also opening avenues for further research in adaptive control and deformable object simulation."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Learning structured models for robotic manipulation of deformable objects"]}]}],"canonical_facts":{"dc:contributor":["Hauser, Kris"],"dc:creator":["Li, Baoyu"],"dc:date":["2025-05-06","2025-05"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","The student, Baoyu Li, accepted the attached license on 2025-05-05 at 15:01.","The student, Baoyu Li, submitted this Thesis for approval on 2025-05-05 at 15:16.","This Thesis was approved for publication on 2025-05-06 at 16:18.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22215 on 2025-10-19 at 18:12:58","Robotic manipulation of deformable objects poses significant challenges due to their complex, nonlinear behaviors, high degrees of freedom, and variable material properties. Traditional model-based techniques are often inadequate at representing these subtleties in the real world, while many learning-based strategies struggle to generalize across different object shapes, poses, and environmental conditions. This thesis presents two novel and unified frameworks that address these challenges through the structured and adaptive modeling of diverse deformable objects. The first framework, Material-Adaptive Graph-Based Neural Dynamics (AdaptiGraph), uses particle-based representations enriched with material type information and continuous physical property parameters. By incorporating these features into a graph neural network and applying a test-time few-shot adaptation strategy, AdaptiGraph delivers precise dynamics predictions and facilitates online estimation of physical properties across various rigid and soft materials. Second, a Particle-Grid Neural Dynamics framework is proposed to directly learn the dynamics of deformable objects from depth images. This approach leverages the innate geometric and physical structure of particle-grid representations to capture fine-scale features, thereby providing accurate predictions of how deformable objects evolve during robotic interactions while remaining robust even under partial observation conditions. Extensive experiments in real-world robotics settings have demonstrated that these frameworks considerably improve manipulation performance in tasks involving 1D, 2D, and 3D deformable objects, including rope straightening, granular material gathering, cloth manipulation, and plush toy relocation. Together, these contributions advance the development of data-efficient robotic systems capable of operating in unstructured environments with diverse deformable materials, while also opening avenues for further research in adaptive control and deformable object simulation."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/129326"],"dc:language":["en","eng"],"dc:rights":["Copyright 2025 Baoyu Li"],"dc:subject":["Robot Learning","Robotic Manipulation","Deformable Object Manipulation","Graph-Based Neural Dynamics"],"dc:title":["Learning structured models for robotic manipulation of deformable objects"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:04Z"}