2026-09-23T18:52:54Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/130228 2026-02-10 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

application/pdf en text 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 Learning goal-conditioned in-hand object re-orientation Niu, Yilong 2025-07-24 Yuan, Wenzhen Dexterous Manipulation Reinforcement Learning eng 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. 2025-08 Text https://hdl.handle.net/2142/130228 Copyright 2025 Yilong Niu Electrical & Computer Eng Electrical & Computer Engr University of Illinois Urbana-Champaign M.S. Thesis