Domain of Attraction Expansion for Physics-Based Character Control

© ACM, YYYY. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM Transactions on Graphics, {VOL#, ISS#, (DATE)} http://doi.acm.org/10.1145/nnnnnn.nnnnnnDetermining effective control strategies and solutions for high degree-of-freedom humanoid characters has been a difficult, ongoing problem. A controller is only valid for a certain set of states of the character, known as the domain of attraction (DOA). This paper shows how states that are initially outside the DOA can be brought inside it. Our first contribution is to show how DOA expansion can be performed for a high-dimensional simulated character. Our second contribution is to present an algorithm that efficiently increases the DOA using random trees that provide denser coverage than the trees produced by typical sampling-based motion planning algorithms. The trees are constructed offline, but can be queried fast enough for near real-time control. We show the effect of DOA expansion on getting up, crouch-to- stand, jumping, and standing-twist controllers. We also show how DOA expansion can be used to connect controllers together