{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/112968"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/112968","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Multi-Objective Bilevel Bayesian optimization for robot and behavior co-design","abstract":"Traditionally, the robot design process is based on the trial-and-error approach that involves repeated cycles of design, prototyping, and evaluation. During the process, the robot designer should tackle multiple objectives, which are commonly in conflicting relationships. Furthermore, the robot design assessment involves costly behavior optimization and performance evaluation in multiple environments. We propose a Multi-Objective Bilevel Bayesian optimization (MO-BBO) algorithm to automate the co-design process of the robot design and behavior simultaneously. Since the behavior should be optimized for each design and environment, we select the next design candidate in a bilevel manner. Design parameters and behavior parameters are the high- and low-level decision variables, respectively. We applied our algorithm to two robot co-design problems: gripper design problem and robot arm placement problem. MO-BBO was able to effectively expand the Pareto front in objective space on two problems. We extend the robot arm placement problem by integrating human-likeness into objectives. To account for human likeness, we construct trajectory-based metrics to evaluate how well the robot arm follows the human motion trajectories extracted from the TUM Kitchen dataset and how similar the robot arm structure is to the human arm structure while following the trajectories. Compared to the designs generated by using reachability indices, the designs generated by the trajectory-based metrics have better performance when following human motion trajectories, especially in terms of collision rate and structural similarity.","abstract_html":"Traditionally, the robot design process is based on the trial-and-error approach that involves repeated cycles of design, prototyping, and evaluation. During the process, the robot designer should tackle multiple objectives, which are commonly in conflicting relationships. Furthermore, the robot design assessment involves costly behavior optimization and performance evaluation in multiple environments. We propose a Multi-Objective Bilevel Bayesian optimization (MO-BBO) algorithm to automate the co-design process of the robot design and behavior simultaneously. Since the behavior should be optimized for each design and environment, we select the next design candidate in a bilevel manner. Design parameters and behavior parameters are the high- and low-level decision variables, respectively. We applied our algorithm to two robot co-design problems: gripper design problem and robot arm placement problem. MO-BBO was able to effectively expand the Pareto front in objective space on two problems. We extend the robot arm placement problem by integrating human-likeness into objectives. To account for human likeness, we construct trajectory-based metrics to evaluate how well the robot arm follows the human motion trajectories extracted from the TUM Kitchen dataset and how similar the robot arm structure is to the human arm structure while following the trajectories. Compared to the designs generated by using reachability indices, the designs generated by the trajectory-based metrics have better performance when following human motion trajectories, especially in terms of collision rate and structural similarity.","abstract_has_math":false,"creators":["Kim, Yeonju"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Mechanical Engineering","degree_department":null,"school":null,"contributors":["Hauser, Kris","Ramos, João"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-01-12T21:45:20Z","date_published":"2022-01-12T21:45:20Z","updated_at":"2026-07-22T22:24:52Z","subjects":["Robot Design Optimization","Robot Arm Placement","Gripper Design"],"languages":["en"],"rights":["Copyright 2021 Yeonju Kim"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/112968","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Hauser, Kris","Ramos, João"]},{"key":"dc:creator","label":"Author","values":["Kim, Yeonju"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-01-12T21:45:20Z","2021-06-25","2021-08"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering"]},{"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":["Robot Design Optimization","Robot Arm Placement","Gripper Design"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2021 Yeonju Kim"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/112968"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Traditionally, the robot design process is based on the trial-and-error approach that involves repeated cycles of design, prototyping, and evaluation. During the process, the robot designer should tackle multiple objectives, which are commonly in conflicting relationships. Furthermore, the robot design assessment involves costly behavior optimization and performance evaluation in multiple environments. We propose a Multi-Objective Bilevel Bayesian optimization (MO-BBO) algorithm to automate the co-design process of the robot design and behavior simultaneously. Since the behavior should be optimized for each design and environment, we select the next design candidate in a bilevel manner. Design parameters and behavior parameters are the high- and low-level decision variables, respectively. We applied our algorithm to two robot co-design problems: gripper design problem and robot arm placement problem. MO-BBO was able to effectively expand the Pareto front in objective space on two problems. We extend the robot arm placement problem by integrating human-likeness into objectives. To account for human likeness, we construct trajectory-based metrics to evaluate how well the robot arm follows the human motion trajectories extracted from the TUM Kitchen dataset and how similar the robot arm structure is to the human arm structure while following the trajectories. Compared to the designs generated by using reachability indices, the designs generated by the trajectory-based metrics have better performance when following human motion trajectories, especially in terms of collision rate and structural similarity.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-01-12 without embargo terms","The student, Yeonju Kim, accepted the attached license on 2021-06-21 at 21:27.","The student, Yeonju Kim, submitted this Thesis for approval on 2021-06-21 at 21:40.","This Thesis was approved for publication on 2021-06-25 at 11:13.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16705 on 2022-01-12 at 12:43:28","Made available in DSpace on 2022-01-12T21:45:20Z (GMT). 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We propose a Multi-Objective Bilevel Bayesian optimization (MO-BBO) algorithm to automate the co-design process of the robot design and behavior simultaneously. Since the behavior should be optimized for each design and environment, we select the next design candidate in a bilevel manner. Design parameters and behavior parameters are the high- and low-level decision variables, respectively. We applied our algorithm to two robot co-design problems: gripper design problem and robot arm placement problem. MO-BBO was able to effectively expand the Pareto front in objective space on two problems. We extend the robot arm placement problem by integrating human-likeness into objectives. To account for human likeness, we construct trajectory-based metrics to evaluate how well the robot arm follows the human motion trajectories extracted from the TUM Kitchen dataset and how similar the robot arm structure is to the human arm structure while following the trajectories. Compared to the designs generated by using reachability indices, the designs generated by the trajectory-based metrics have better performance when following human motion trajectories, especially in terms of collision rate and structural similarity.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-01-12 without embargo terms","The student, Yeonju Kim, accepted the attached license on 2021-06-21 at 21:27.","The student, Yeonju Kim, submitted this Thesis for approval on 2021-06-21 at 21:40.","This Thesis was approved for publication on 2021-06-25 at 11:13.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16705 on 2022-01-12 at 12:43:28","Made available in DSpace on 2022-01-12T21:45:20Z (GMT). No. of bitstreams: 2 KIM-THESIS-2021.pdf: 9096247 bytes, checksum: 6b09f8bfe41eeb093815c40c3c047cf3 (MD5) LICENSE.txt: 4207 bytes, checksum: 5a7819e955dc30b8a4ccd0fd2a06a73c (MD5) Previous issue date: 2021-06-25"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/112968"],"dc:language":["en"],"dc:rights":["Copyright 2021 Yeonju Kim"],"dc:subject":["Robot Design Optimization","Robot Arm Placement","Gripper Design"],"dc:title":["Multi-Objective Bilevel Bayesian optimization for robot and behavior co-design"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Mechanical Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:52Z"}