{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/129393"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/129393","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Learning for open-world mobile robots","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":["Wasserman, Justin"],"institution":"University of Illinois Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Chowdhary, Girish","Driggs-Campbell, Katie","Schwing, Alexander","Wang, Shenlong"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-04-15","date_published":"2025-04-15","updated_at":"2026-07-22T22:25:05Z","subjects":["robotics","embodied ai","simulator","navigation","open-world","artificial intelligence","computer vision","machine learning"],"languages":["en","eng"],"rights":["Copyright 2025 Justin Wasserman"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/129393","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Chowdhary, Girish","Driggs-Campbell, Katie","Schwing, Alexander","Wang, Shenlong"]},{"key":"dc:creator","label":"Author","values":["Wasserman, Justin"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-04-15","2025-05"]},{"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":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"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":["robotics","embodied ai","simulator","navigation","open-world","artificial intelligence","computer vision","machine learning"]}]},{"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 Justin Wasserman"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/129393"]}]},{"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, Justin Wasserman, accepted the attached license on 2025-04-13 at 13:29.","The student, Justin Wasserman, submitted this Dissertation for approval on 2025-04-13 at 13:36.","This Dissertation was approved for publication on 2025-04-15 at 06:55.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21753 on 2025-10-19 at 18:18:07","Deploying robots from a lab setting to open-world environments requires understanding semantic information and leveraging large data sources. Various paradigms have been introduced to tackle semantic visual-goal navigation, where an agent is placed in a random environment and must reach a goal. We first decompose this task into two components: (1) a data-driven exploration policy that learns semantics and environmental relations and (2) a geometric-based policy specialized for goal-directed navigation. Beyond this decomposition, we investigate whether further structure can enhance performance. To this end, we retrain the exploration policy with guidance from the geometric policy. Additionally, we explore a sim-to-real approach to improve state estimation for legged robots, enabling robust odometry prediction across diverse scenarios. This thesis presents real-world experiments supporting each of these works in mobile robotics. Future work is finally discussed towards the development of a foundation model for navigation."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Learning for open-world mobile robots"]}]}],"canonical_facts":{"dc:contributor":["Chowdhary, Girish","Driggs-Campbell, Katie","Schwing, Alexander","Wang, Shenlong"],"dc:creator":["Wasserman, Justin"],"dc:date":["2025-04-15","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, Justin Wasserman, accepted the attached license on 2025-04-13 at 13:29.","The student, Justin Wasserman, submitted this Dissertation for approval on 2025-04-13 at 13:36.","This Dissertation was approved for publication on 2025-04-15 at 06:55.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21753 on 2025-10-19 at 18:18:07","Deploying robots from a lab setting to open-world environments requires understanding semantic information and leveraging large data sources. Various paradigms have been introduced to tackle semantic visual-goal navigation, where an agent is placed in a random environment and must reach a goal. We first decompose this task into two components: (1) a data-driven exploration policy that learns semantics and environmental relations and (2) a geometric-based policy specialized for goal-directed navigation. Beyond this decomposition, we investigate whether further structure can enhance performance. To this end, we retrain the exploration policy with guidance from the geometric policy. Additionally, we explore a sim-to-real approach to improve state estimation for legged robots, enabling robust odometry prediction across diverse scenarios. This thesis presents real-world experiments supporting each of these works in mobile robotics. Future work is finally discussed towards the development of a foundation model for navigation."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/129393"],"dc:language":["en","eng"],"dc:rights":["Copyright 2025 Justin Wasserman"],"dc:subject":["robotics","embodied ai","simulator","navigation","open-world","artificial intelligence","computer vision","machine learning"],"dc:title":["Learning for open-world mobile robots"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:05Z"}