{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/130228"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/130228","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Learning goal-conditioned in-hand object re-orientation","abstract":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-08-01","abstract_html":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-08-01","abstract_has_math":false,"creators":["Niu, Yilong"],"institution":"University of Illinois Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Yuan, Wenzhen"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-07-24","date_published":"2025-07-24","updated_at":"2026-07-22T22:25:06Z","subjects":["Dexterous Manipulation","Reinforcement Learning"],"languages":["en","eng"],"rights":["Copyright 2025 Yilong Niu"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/130228","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Yuan, Wenzhen"]},{"key":"dc:creator","label":"Author","values":["Niu, Yilong"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-07-24","2025-08"]},{"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 Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Dexterous Manipulation","Reinforcement 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 Yilong Niu"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/130228"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-08-01","The student, Yilong Niu, accepted the attached license on 2025-07-24 at 12:01.","The student, Yilong Niu, submitted this Thesis for approval on 2025-07-24 at 12:11.","This Thesis was approved for publication on 2025-07-24 at 13:20.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22726 on 2025-10-25 at 15:54:36","Dexterous manipulation using anthropomorphic hands is a critical capability for general-purpose robots operating in human-centric, unstructured environments. However, controlling a dexterous hand to perform contact-rich tasks remains challenging, due to its high degrees of freedom and the need to maintain force-closure. While classical model-based methods have shown promising results, their applicability is often constrained by strong assumptions that limit generalization. In this thesis, we address the problem of goal-conditioned in-hand object re-orientation over the full SO(3) space, in contrast to prior works that restrict rotation to specific axes. We propose a learning-based framework that combines model-free reinforcement learning with a teacher-student paradigm and automatic domain randomization to train a unified policy for in-hand object rotation. Our policy relies only on proprioceptive feedback and object pose tracking, without requiring prior knowledge of object shape. We evaluate the approach extensively in simulation, demonstrating strong generalization to objects with unseen shapes and physical properties. Furthermore, we validate the policy on a low-cost, open-source dexterous hand, achieving successful zero-shot transfer to the real world."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Learning goal-conditioned in-hand object re-orientation"]}]}],"canonical_facts":{"dc:contributor":["Yuan, Wenzhen"],"dc:creator":["Niu, Yilong"],"dc:date":["2025-07-24","2025-08"],"dc:description":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-08-01","The student, Yilong Niu, accepted the attached license on 2025-07-24 at 12:01.","The student, Yilong Niu, submitted this Thesis for approval on 2025-07-24 at 12:11.","This Thesis was approved for publication on 2025-07-24 at 13:20.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22726 on 2025-10-25 at 15:54:36","Dexterous manipulation using anthropomorphic hands is a critical capability for general-purpose robots operating in human-centric, unstructured environments. However, controlling a dexterous hand to perform contact-rich tasks remains challenging, due to its high degrees of freedom and the need to maintain force-closure. While classical model-based methods have shown promising results, their applicability is often constrained by strong assumptions that limit generalization. In this thesis, we address the problem of goal-conditioned in-hand object re-orientation over the full SO(3) space, in contrast to prior works that restrict rotation to specific axes. We propose a learning-based framework that combines model-free reinforcement learning with a teacher-student paradigm and automatic domain randomization to train a unified policy for in-hand object rotation. Our policy relies only on proprioceptive feedback and object pose tracking, without requiring prior knowledge of object shape. We evaluate the approach extensively in simulation, demonstrating strong generalization to objects with unseen shapes and physical properties. Furthermore, we validate the policy on a low-cost, open-source dexterous hand, achieving successful zero-shot transfer to the real world."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/130228"],"dc:language":["en","eng"],"dc:rights":["Copyright 2025 Yilong Niu"],"dc:subject":["Dexterous Manipulation","Reinforcement Learning"],"dc:title":["Learning goal-conditioned in-hand object re-orientation"],"dc:type":["text"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:06Z"}