{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/120169"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/120169","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Learning from physical human-robot interaction with velocity-controlled instantaneous responses and update thresholds for noise rejection","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms","abstract_has_math":false,"creators":["Xie, Yiqing"],"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 Rose"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-05","date_published":"2023-05","updated_at":"2026-07-22T22:24:56Z","subjects":["Phri","Robotics","Intention Recognition"],"languages":["en","eng"],"rights":["Copyright 2023 Yiqing Xie"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/120169","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Driggs-Campbell, Katherine Rose"]},{"key":"dc:creator","label":"Author","values":["Xie, Yiqing"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-05","2023-05-03"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"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":["Phri","Robotics","Intention Recognition"]}]},{"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 2023 Yiqing Xie"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/120169"]}]},{"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 2023-09-01 without embargo terms","The student, Yiqing Xie, accepted the attached license on 2023-05-03 at 11:10.","The student, Yiqing Xie, submitted this Thesis for approval on 2023-05-03 at 11:58.","This Thesis was approved for publication on 2023-05-03 at 12:48.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19314 on 2023-09-01 at 16:56:13","The physical human-robot interaction (pHRI) is an important means for humans to control robots in real-time. However, human touch may or may not be intentional. Current approaches react to these force inputs regardless of whether they are intended to be meaningful. The robot will also retain its behavior logic after the human’s interference. Recent research has shown that robots can learn from pHRI and adjust their behavior logic in real-time. We replicate and extend a state-of-the-art impedance-controlled approach to the pHRI problem on a platform without torque control. We believe that the data generated by pHRI can reveal a user’s true in-tentions, such as waypoints to pass or obstacles to avoid. To examine the connection between pHRI and intent, we first create an experimental testbed for pHRI with a UR5e platform. Then, using the data we collect, we estimate the human’s intentions from the force input and translate these intentions into potential features for the robot to learn. Through these steps, the robot can understand the human’s goal. Extending prior art in this type of learn-ing, we propose a new update rule for the robot to learn human intentions that takes into account unintentional forces, making the learning process more robust."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Learning from physical human-robot interaction with velocity-controlled instantaneous responses and update thresholds for noise rejection"]}]}],"canonical_facts":{"dc:contributor":["Driggs-Campbell, Katherine Rose"],"dc:creator":["Xie, Yiqing"],"dc:date":["2023-05","2023-05-03"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms","The student, Yiqing Xie, accepted the attached license on 2023-05-03 at 11:10.","The student, Yiqing Xie, submitted this Thesis for approval on 2023-05-03 at 11:58.","This Thesis was approved for publication on 2023-05-03 at 12:48.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19314 on 2023-09-01 at 16:56:13","The physical human-robot interaction (pHRI) is an important means for humans to control robots in real-time. However, human touch may or may not be intentional. Current approaches react to these force inputs regardless of whether they are intended to be meaningful. The robot will also retain its behavior logic after the human’s interference. Recent research has shown that robots can learn from pHRI and adjust their behavior logic in real-time. We replicate and extend a state-of-the-art impedance-controlled approach to the pHRI problem on a platform without torque control. We believe that the data generated by pHRI can reveal a user’s true in-tentions, such as waypoints to pass or obstacles to avoid. To examine the connection between pHRI and intent, we first create an experimental testbed for pHRI with a UR5e platform. Then, using the data we collect, we estimate the human’s intentions from the force input and translate these intentions into potential features for the robot to learn. Through these steps, the robot can understand the human’s goal. Extending prior art in this type of learn-ing, we propose a new update rule for the robot to learn human intentions that takes into account unintentional forces, making the learning process more robust."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/120169"],"dc:language":["en","eng"],"dc:rights":["Copyright 2023 Yiqing Xie"],"dc:subject":["Phri","Robotics","Intention Recognition"],"dc:title":["Learning from physical human-robot interaction with velocity-controlled instantaneous responses and update thresholds for noise rejection"],"dc:type":["text"],"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":"2026-07-22T22:24:56Z"}