GPU-Based Contact-Aware Trajectory Optimization Using A Smooth Force Model
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Date
2019
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
ACM
Abstract
We present a new formulation of trajectory optimization for articulated bodies. Our approach uses a fully differentiable dynamic model of the articulated body, and a smooth force model that approximates all kinds of internal/external forces as a smooth function of the articulated body's kinematic state. Our formulation is contact-aware and its complexity is not dependent on the contact positions or the number of contacts. Furthermore, we exploit the block-tridiagonal structure of the Hessian matrix and present a highly parallel Newton-type trajectory optimizer that maps well to GPU architectures. Moreover, we use a Markovian regularization term to overcome the local minima problems in the optimization formulation. We highlight the performance of our approach using a set of locomotion tasks performed by characters with 15 − 35 DOFs. In practice, our GPU-based algorithm running on a NVIDIA TITAN-X GPU provides more than 30× speedup over a multi-core CPU-based implementation running on Intel Xeon E5-1620 CPU. In addition, we demonstrate applications of our method on various applications such as contact-rich motion planning, receding-horizon control, and motion graph construction.
Description
@inproceedings{10.1145:3309486.3340246,
booktitle = {Eurographics/ ACM SIGGRAPH Symposium on Computer Animation},
editor = {Batty, Christopher and Huang, Jin},
title = {{GPU-Based Contact-Aware Trajectory Optimization Using A Smooth Force Model}},
author = {Pan, Zherong and Ren, Bo and Manocha, Dinesh},
year = {2019},
publisher = {ACM},
ISSN = {1727-5288},
ISBN = {978-1-4503-6677-9},
DOI = {10.1145/3309486.3340246}
}