Director of Research (if dissertation) or Advisor (if thesis)
Yuan, Wenzhen
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Dexterous Manipulation
Reinforcement Learning
Language
eng
Abstract
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.
Use this login method if you
don't
have an
@illinois.edu
email address.
(Oops, I do have one)
IDEALS migrated to a new platform on June 23, 2022. If you created
your account prior to this date, you will have to reset your password
using the forgot-password link below.
