Learning Task-Agnostic Action Spaces for Movement Optimization
We propose a novel method for exploring the dynamics of physically based animated characters, and learning a task-agnostic action space that makes movement optimization easier. Like several previous article, we parameterize actions as target states, and learn a short-horizon goal-conditioned low-lev...
| Veröffentlicht in: | IEEE transactions on visualization and computer graphics. - 1996. - 28(2022), 12 vom: 01. Dez., Seite 4700-4712 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2022
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on visualization and computer graphics |
| Schlagworte: | Journal Article |
| Online verfügbar |
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