Learning and Exploring Motor Skills with Spacetime Bounds
Date
2021Author
Ma, Li-Ke
Yang, Zeshi
Tong, Xin
Guo, Baining
Yin, KangKang
Metadata
Show full item recordAbstract
Equipping characters with diverse motor skills is the current bottleneck of physics-based character animation. We propose a Deep Reinforcement Learning (DRL) framework that enables physics-based characters to learn and explore motor skills from reference motions. The key insight is to use loose space-time constraints, termed spacetime bounds, to limit the search space in an early termination fashion. As we only rely on the reference to specify loose spacetime bounds, our learning is more robust with respect to low quality references. Moreover, spacetime bounds are hard constraints that improve learning of challenging motion segments, which can be ignored by imitation-only learning. We compare our method with state-of-the-art tracking-based DRL methods. We also show how to guide style exploration within the proposed framework.
BibTeX
@article {10.1111:cgf.142630,
journal = {Computer Graphics Forum},
title = {{Learning and Exploring Motor Skills with Spacetime Bounds}},
author = {Ma, Li-Ke and Yang, Zeshi and Tong, Xin and Guo, Baining and Yin, KangKang},
year = {2021},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {10.1111/cgf.142630}
}
journal = {Computer Graphics Forum},
title = {{Learning and Exploring Motor Skills with Spacetime Bounds}},
author = {Ma, Li-Ke and Yang, Zeshi and Tong, Xin and Guo, Baining and Yin, KangKang},
year = {2021},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {10.1111/cgf.142630}
}