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Learning predict-and-simulate policies from unorganized human motion data

Cited 80 time in Web of Science Cited 99 time in Scopus
Authors

Park, Soohwan; Ryu, Hoseok; Lee, Seyoung; Lee, Sunmin; Lee, Jehee

Issue Date
2019-11
Publisher
Association for Computing Machinary, Inc.
Citation
ACM Transactions on Graphics, Vol.38 No.6, p. 205
Abstract
The goal of this research is to create physically simulated biped characters equipped with a rich repertoire of motor skills. The user can control the characters interactively by modulating their control objectives. The characters can interact physically with each other and with the environment. We present a novel network-based algorithm that learns control policies from unorganized, minimally-labeled human motion data. The network architecture for interactive character animation incorporates an RNN-based motion generator into a DRL-based controller for physics simulation and control. The motion generator guides forward dynamics simulation by feeding a sequence of future motion frames to track. The rich future prediction facilitates policy learning from large training data sets. We will demonstrate the effectiveness of our approach with biped characters that learn a variety of dynamic motor skills from large, unorganized data and react to unexpected perturbation beyond the scope of the training data.
ISSN
0730-0301
URI
https://hdl.handle.net/10371/179322
DOI
https://doi.org/10.1145/3355089.3356501
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