{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/127288"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/127288","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Data-driven bimanual catching: Integrating model-based and learning-based approaches for dynamic object grasping","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-03-28 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-03-28 without embargo terms","abstract_has_math":false,"creators":["Xu, Yiyang"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Driggs-Campbell, Katherine"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-12","date_published":"2024-12","updated_at":"2026-07-22T22:25:03Z","subjects":["Bimanual Robotic Arms","Robotic Catching"],"languages":["en","eng"],"rights":["Copyright 2024 Yiyang Xu"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/127288","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Driggs-Campbell, Katherine"]},{"key":"dc:creator","label":"Author","values":["Xu, Yiyang"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-12","2024-12-12"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"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 at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Bimanual Robotic Arms","Robotic Catching"]}]},{"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 2024 Yiyang Xu"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/127288"]}]},{"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 2025-03-28 without embargo terms","The student, Yiyang Xu, accepted the attached license on 2024-12-09 at 20:20.","The student, Yiyang Xu, submitted this Thesis for approval on 2024-12-09 at 20:45.","This Thesis was approved for publication on 2024-12-12 at 13:26.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21558 on 2025-03-28 at 14:28:36","This thesis presents a novel approach to dynamic object catching using a bimanual robotic system, which integrates both model-based and data-driven methods. Traditional unimanual robotic manipulation systems have limitations in handling large, complex, fast-moving, or irregularly shaped objects. Catching with two hands is more intuitive to humans as it provides greater stability and balance compared to using one hand. We aim to have the robotic system mimic human motion patterns, making it capable of handling a broader range of tasks. We propose a teacher-student policy training framework consisting of a model-based teacher policy and a learning-based student policy. Both policies take raw camera input and output the target grippers' poses for catching objects in flight. The model-based approach utilizes a sampling based technique to generate optimal grasp candidates, and it serves as both a baseline and a foundation for data collection used by the data-driven system. The data-driven approach incorporates PointNet++ and a Transformer encoder to process spatial-temporal point cloud data. Additionally, the network is combined with a physics-based ballistic motion model to estimate the object’s landing position. The deep learning model predicts the 6D rotation for both grippers and 3D translation residuals compensated for object-specific dynamic not captured by the physics-based model. The proposed data-driven system is evaluated in a simulated environment, and it demonstrates significant improvements in grasp success rates and generalization across various object types compared to the baseline model-based method. The results highlight the potential for deploying such a system in real-world applications."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Data-driven bimanual catching: Integrating model-based and learning-based approaches for dynamic object grasping"]}]}],"canonical_facts":{"dc:contributor":["Driggs-Campbell, Katherine"],"dc:creator":["Xu, Yiyang"],"dc:date":["2024-12","2024-12-12"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-03-28 without embargo terms","The student, Yiyang Xu, accepted the attached license on 2024-12-09 at 20:20.","The student, Yiyang Xu, submitted this Thesis for approval on 2024-12-09 at 20:45.","This Thesis was approved for publication on 2024-12-12 at 13:26.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21558 on 2025-03-28 at 14:28:36","This thesis presents a novel approach to dynamic object catching using a bimanual robotic system, which integrates both model-based and data-driven methods. Traditional unimanual robotic manipulation systems have limitations in handling large, complex, fast-moving, or irregularly shaped objects. Catching with two hands is more intuitive to humans as it provides greater stability and balance compared to using one hand. We aim to have the robotic system mimic human motion patterns, making it capable of handling a broader range of tasks. We propose a teacher-student policy training framework consisting of a model-based teacher policy and a learning-based student policy. Both policies take raw camera input and output the target grippers' poses for catching objects in flight. The model-based approach utilizes a sampling based technique to generate optimal grasp candidates, and it serves as both a baseline and a foundation for data collection used by the data-driven system. The data-driven approach incorporates PointNet++ and a Transformer encoder to process spatial-temporal point cloud data. Additionally, the network is combined with a physics-based ballistic motion model to estimate the object’s landing position. The deep learning model predicts the 6D rotation for both grippers and 3D translation residuals compensated for object-specific dynamic not captured by the physics-based model. The proposed data-driven system is evaluated in a simulated environment, and it demonstrates significant improvements in grasp success rates and generalization across various object types compared to the baseline model-based method. The results highlight the potential for deploying such a system in real-world applications."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/127288"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Yiyang Xu"],"dc:subject":["Bimanual Robotic Arms","Robotic Catching"],"dc:title":["Data-driven bimanual catching: Integrating model-based and learning-based approaches for dynamic object grasping"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:03Z"}