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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Ph.D. Dissertation Defense Announcement
Title: Developing Agile Motor Skills on Virtual and Real Humanoids
Sehoon Ha
School of Interactive Computing
Georgia Institute of Technology
http://www.cc.gatech.edu/~sha9/
Date: Tuesday, July 28, 2015
Time: 12:00pm - 2:30pm EDT
Location: TSRB 223
Committee:
Dr. C. Karen Liu (Advisor, School of Interactive Computing, Georgia Institute of Technology)
Dr. Greg Turk (School of Interactive Computing, Georgia Institute of Technology)
Dr. Jarek Rossignac (School of Interactive Computing, Georgia Institute of Technology)
Dr. Jun Ueda (School of Mechanical Engineering, Georgia Institute of Technology)
Dr. Katsu Yamane (Disney Research Pittsburgh)
Abstract:
Demonstrating strength and agility on virtual and real humanoids has been an important goal in computer graphics and robotics. However, developing physics-based controllers for various agile motor skills requires a tremendous amount of prior knowledge and manual labor due to its
complex mechanisms. The focus of the dissertation is to develop a set of computational tools to
expedite the design process of physics-based controllers that can execute a variety of agile motor skills on virtual and real humanoids. Instead of directly deploying motions on real humanoids, this dissertation takes an approach that develops appropriate theories and models in virtual simulation and systematically transfers the solutions to hardware systems.
The algorithms and frameworks in this dissertation span various topics from specific physics-based controllers to general learning frameworks. We first present an online algorithm for controlling falling and landing motions of virtual characters. The proposed algorithm is effective and efficient enough to generate falling motions for a wide range of arbitrary initial conditions in real-time. Next, we present a robust falling strategy for real humanoids that can manage a wide range of perturbations by planning the optimal contact sequences. We then introduce an iterative learning framework to intuitively design various agile motions, which is inspired by human learning techniques. The proposed framework is followed by novel algorithms to efficiently optimize control parameters for the target tasks, especially when they have many constraints or parameterized goals. Finally, we introduce an iterative approach for exporting simulation-optimized control policies to hardware of robots to reduce the number of hardware experiments, that accompany expensive costs and labors.
