{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/124136"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/124136","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Robot learning from videos","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","abstract_has_math":false,"creators":["Chang, Matthew"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Gupta, Saurabh","Forsyth, David","Lazebnik, Svetlana","Chaplot, Devendra"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-05","date_published":"2024-05","updated_at":"2026-07-22T22:25:00Z","subjects":["Computer Vision","Robotics","Robot Learning"],"languages":["en","eng"],"rights":["Copyright 2024 Matthew Chang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/124136","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Gupta, Saurabh","Forsyth, David","Lazebnik, Svetlana","Chaplot, Devendra"]},{"key":"dc:creator","label":"Author","values":["Chang, Matthew"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-05","2024-03-08"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"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 at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Computer Vision","Robotics","Robot 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 2024 Matthew Chang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/124136"]}]},{"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 2024-09-16 without embargo terms","The student, Matthew Chang, accepted the attached license on 2024-02-13 at 12:54.","The student, Matthew Chang, submitted this Dissertation for approval on 2024-02-13 at 13:02.","This Dissertation was approved for publication on 2024-03-08 at 15:41.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20216 on 2024-09-16 at 00:33:05","State-of-the-art machine learning models are extremely powerful and are finally breaking through into commercial products for computer vision and natural language processing. One common factor among these successful models is, they all require massive datasets for training. Following this trend, large-scale learning-based methods present a promising way forward for robotics research. This line of thinking naturally raises two questions: from where can we collect the appropriate data? and, how can it be leveraged to create effective robotic systems? Fortunately, a vast amount of data already exists, showcasing the complexity of real-world environments and interactions that robots need to understand, in the form of videos. However, these video sources of data cannot be directly used with the techniques traditionally applied for robot learning. Videos may lack explicit action or goal labels, often depict suboptimal trajectories, and present a significant embodiment gap, both visually and in dynamics. These challenges underscore the need for new robot learning methods that can overcome these obstacles. In this work, we present our efforts to realize the goal of robot learning at scale using in-the-wild videos, developing methods to address each of the challenges that limit robot learning from videos. We introduce techniques for inferring actions and goals in unlabelled video data, learning optimal behavior from sub-optimal data, and tackling the embodiment gap by leveraging factored representations. Overall, this dissertation lays the foundations for how video data can be leveraged for robot learning at scale. We hope this work can serve as a step towards general robotic agents that can make significant positive impacts in the world."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Robot learning from videos"]}]}],"canonical_facts":{"dc:contributor":["Gupta, Saurabh","Forsyth, David","Lazebnik, Svetlana","Chaplot, Devendra"],"dc:creator":["Chang, Matthew"],"dc:date":["2024-05","2024-03-08"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","The student, Matthew Chang, accepted the attached license on 2024-02-13 at 12:54.","The student, Matthew Chang, submitted this Dissertation for approval on 2024-02-13 at 13:02.","This Dissertation was approved for publication on 2024-03-08 at 15:41.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20216 on 2024-09-16 at 00:33:05","State-of-the-art machine learning models are extremely powerful and are finally breaking through into commercial products for computer vision and natural language processing. One common factor among these successful models is, they all require massive datasets for training. Following this trend, large-scale learning-based methods present a promising way forward for robotics research. This line of thinking naturally raises two questions: from where can we collect the appropriate data? and, how can it be leveraged to create effective robotic systems? Fortunately, a vast amount of data already exists, showcasing the complexity of real-world environments and interactions that robots need to understand, in the form of videos. However, these video sources of data cannot be directly used with the techniques traditionally applied for robot learning. Videos may lack explicit action or goal labels, often depict suboptimal trajectories, and present a significant embodiment gap, both visually and in dynamics. These challenges underscore the need for new robot learning methods that can overcome these obstacles. In this work, we present our efforts to realize the goal of robot learning at scale using in-the-wild videos, developing methods to address each of the challenges that limit robot learning from videos. We introduce techniques for inferring actions and goals in unlabelled video data, learning optimal behavior from sub-optimal data, and tackling the embodiment gap by leveraging factored representations. Overall, this dissertation lays the foundations for how video data can be leveraged for robot learning at scale. We hope this work can serve as a step towards general robotic agents that can make significant positive impacts in the world."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/124136"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Matthew Chang"],"dc:subject":["Computer Vision","Robotics","Robot Learning"],"dc:title":["Robot learning from videos"],"dc:type":["text"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:00Z"}