{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/132495"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/132495","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Learning-based shared control for open world, dexterous robot teleoperation","abstract":"Teleoperation allows people to extend their perception and interaction capabilities to remote locations in cases where distance or safety constraints prevent them from physically traveling. It is especially useful for operation in “open-world” environments, environments that cannot be accurately modeled ahead of time, where traditional automation techniques often prove unreliable. However, while humans are extremely adept at a wide variety of manipulation tasks, transferring these skills through a robot has remained an open challenge for over half a century. Given constraints on an operator’s total cognitive load, high degree-of-freedom systems face a tradeoff between flexibility and precision. Giving the operator direct control over every joint on the robot provides them with the maximum flexibility to complete many kinds of tasks, but the high cognitive load associated with such an interface makes it impossible to properly coordinate the joints for tasks requiring high precision. In contrast, shared control systems that let the operator control the robot at a higher level of abstraction reduce this cognitive load and can improve the operator’s precision, but simultaneously reduce the flexibility of the interface to accomplish tasks for which it was not explicitly designed. Towards the goal of creating truly telepresent systems, this thesis introduces several learning techniques to dynamically adapt robotic teleoperation interfaces to specific users and tasks, allowing operators to more naturally express their intent and enabling greater transfer of their manipulation skills. This approach has lead to improvements in mappings for retargeting operator inputs to robot actions, harnessing the operator’s existing manipulation skills to flexibly complete many kinds of tasks while maintaining a high degree of precision.","abstract_html":"Teleoperation allows people to extend their perception and interaction capabilities to remote locations in cases where distance or safety constraints prevent them from physically traveling. It is especially useful for operation in “open-world” environments, environments that cannot be accurately modeled ahead of time, where traditional automation techniques often prove unreliable. However, while humans are extremely adept at a wide variety of manipulation tasks, transferring these skills through a robot has remained an open challenge for over half a century. Given constraints on an operator’s total cognitive load, high degree-of-freedom systems face a tradeoff between flexibility and precision. Giving the operator direct control over every joint on the robot provides them with the maximum flexibility to complete many kinds of tasks, but the high cognitive load associated with such an interface makes it impossible to properly coordinate the joints for tasks requiring high precision. In contrast, shared control systems that let the operator control the robot at a higher level of abstraction reduce this cognitive load and can improve the operator’s precision, but simultaneously reduce the flexibility of the interface to accomplish tasks for which it was not explicitly designed. Towards the goal of creating truly telepresent systems, this thesis introduces several learning techniques to dynamically adapt robotic teleoperation interfaces to specific users and tasks, allowing operators to more naturally express their intent and enabling greater transfer of their manipulation skills. This approach has lead to improvements in mappings for retargeting operator inputs to robot actions, harnessing the operator’s existing manipulation skills to flexibly complete many kinds of tasks while maintaining a high degree of precision.","abstract_has_math":false,"creators":["Naughton, Patrick"],"institution":"University of Illinois Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Hauser, Kris","Bretl, Timothy","Lazebnik, Svetlana","Pinto, Lerrel","Srinivasa, Siddhartha"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-12","date_published":"2025-12","updated_at":"2026-07-22T22:25:07Z","subjects":["Teleoperation","Shared Control","Dexterous Manipulation","Machine Learning"],"languages":["en"],"rights":["Copyright 2025 Patrick Naughton"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/132495","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Hauser, Kris","Bretl, Timothy","Lazebnik, Svetlana","Pinto, Lerrel","Srinivasa, Siddhartha"]},{"key":"dc:creator","label":"Author","values":["Naughton, Patrick"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-12","2025-11-17"]},{"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":["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":["Teleoperation","Shared Control","Dexterous Manipulation","Machine Learning"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2025 Patrick Naughton"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/132495"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Teleoperation allows people to extend their perception and interaction capabilities to remote locations in cases where distance or safety constraints prevent them from physically traveling. It is especially useful for operation in “open-world” environments, environments that cannot be accurately modeled ahead of time, where traditional automation techniques often prove unreliable. However, while humans are extremely adept at a wide variety of manipulation tasks, transferring these skills through a robot has remained an open challenge for over half a century. Given constraints on an operator’s total cognitive load, high degree-of-freedom systems face a tradeoff between flexibility and precision. Giving the operator direct control over every joint on the robot provides them with the maximum flexibility to complete many kinds of tasks, but the high cognitive load associated with such an interface makes it impossible to properly coordinate the joints for tasks requiring high precision. In contrast, shared control systems that let the operator control the robot at a higher level of abstraction reduce this cognitive load and can improve the operator’s precision, but simultaneously reduce the flexibility of the interface to accomplish tasks for which it was not explicitly designed. Towards the goal of creating truly telepresent systems, this thesis introduces several learning techniques to dynamically adapt robotic teleoperation interfaces to specific users and tasks, allowing operators to more naturally express their intent and enabling greater transfer of their manipulation skills. This approach has lead to improvements in mappings for retargeting operator inputs to robot actions, harnessing the operator’s existing manipulation skills to flexibly complete many kinds of tasks while maintaining a high degree of precision.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2026-02-19 without embargo terms","The student, Patrick Naughton, accepted the attached license on 2025-11-16 at 17:34.","The student, Patrick Naughton, submitted this Dissertation for approval on 2025-11-16 at 17:38.","This Dissertation was approved for publication on 2025-11-17 at 13:18.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22874 on 2026-02-19 at 18:24:47"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Learning-based shared control for open world, dexterous robot teleoperation"]}]}],"canonical_facts":{"dc:contributor":["Hauser, Kris","Bretl, Timothy","Lazebnik, Svetlana","Pinto, Lerrel","Srinivasa, Siddhartha"],"dc:creator":["Naughton, Patrick"],"dc:date":["2025-12","2025-11-17"],"dc:description":["Teleoperation allows people to extend their perception and interaction capabilities to remote locations in cases where distance or safety constraints prevent them from physically traveling. It is especially useful for operation in “open-world” environments, environments that cannot be accurately modeled ahead of time, where traditional automation techniques often prove unreliable. However, while humans are extremely adept at a wide variety of manipulation tasks, transferring these skills through a robot has remained an open challenge for over half a century. Given constraints on an operator’s total cognitive load, high degree-of-freedom systems face a tradeoff between flexibility and precision. Giving the operator direct control over every joint on the robot provides them with the maximum flexibility to complete many kinds of tasks, but the high cognitive load associated with such an interface makes it impossible to properly coordinate the joints for tasks requiring high precision. In contrast, shared control systems that let the operator control the robot at a higher level of abstraction reduce this cognitive load and can improve the operator’s precision, but simultaneously reduce the flexibility of the interface to accomplish tasks for which it was not explicitly designed. Towards the goal of creating truly telepresent systems, this thesis introduces several learning techniques to dynamically adapt robotic teleoperation interfaces to specific users and tasks, allowing operators to more naturally express their intent and enabling greater transfer of their manipulation skills. This approach has lead to improvements in mappings for retargeting operator inputs to robot actions, harnessing the operator’s existing manipulation skills to flexibly complete many kinds of tasks while maintaining a high degree of precision.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2026-02-19 without embargo terms","The student, Patrick Naughton, accepted the attached license on 2025-11-16 at 17:34.","The student, Patrick Naughton, submitted this Dissertation for approval on 2025-11-16 at 17:38.","This Dissertation was approved for publication on 2025-11-17 at 13:18.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22874 on 2026-02-19 at 18:24:47"],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/132495"],"dc:language":["en"],"dc:rights":["Copyright 2025 Patrick Naughton"],"dc:subject":["Teleoperation","Shared Control","Dexterous Manipulation","Machine Learning"],"dc:title":["Learning-based shared control for open world, dexterous robot teleoperation"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:07Z"}