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Ph.D. Dissertation Defense Announcement
Title: Locomotion Synthesis in Complex Physically Simulated Environments
Jie Tan
School of Interactive Computing
Georgia Institute of Technology
http://www.cc.gatech.edu/~jtan34/
Date: Wednesday, October 7, 2015
Time: 2:00pm - 4:30pm EDT
Location: TSRB 223
Committee:
Dr. Greg Turk (Advisor, School of Interactive Computing, Georgia Institute of Technology) Dr. Karen Liu (Advisor, School of Interactive Computing, Georgia Institute of Technology) Dr. Jarek Rossignac (School of Interactive Computing, Georgia Institute of Technology) Dr. Frank Dellaert (School of Interactive Computing, Georgia Institute of Technology) Dr. James O'Brien (EECS, University of California at Berkeley)
Abstract:
Understanding and synthesizing locomotion of humans and animals will have far-reaching impacts in computer animation, robotic and biomechanics. However, due to the complexity of the neuromuscular control and physical interactions with the environment, computationally modeling these seemingly effortless locomotion imposes a grand challenge for scientists, engineers and artists. The focus of this dissertation is to present a set of computational tools, including physical simulation and controller optimization, which can automatically synthesize locomotion for humans and animals.
We first present the computational tools to study swimming motions for a wide variety of aquatic animals. This method builds a simulation of two-way interaction between fluid and an articulated rigid body system. It then searches for the most energy efficient way to swim for a given body shape in the simulated hydrodynamic environment. Next, we present an algorithm that can synthesize locomotion of soft body animals that do not have skeleton support. We combine a finite element simulation with a muscle model that is inspired by muscular hydrostat in nature. We then formulate a quadratic program with complementarity condition (QPCC) to optimize the muscle contraction and contact forces that can lead to meaningful locomotion. We develop an efficient QPCC solver that solves a challenging optimization problem at the presence of discontinuous contact events. We also present algorithms to model human locomotion with a passive mechanical device: riding a bicycle. We apply a powerful reinforcement learning algorithm, which can search for both the parametrization and the parameters of a control policy, to enable a virtual human character to perform bicycle stunts in a physically simulated environment. Finally, we explore the possibility to transfer the controllers designed in a simulation to a real humanoid robot. We tackle the challenge of the Reality Gap by calibrating the physical simulation to match the data measured in the real-world experiments.
