Neural probabilistic motor primitives for humanoid control
We focus on the problem of learning a single motor module that can flexibly express a range of behaviors for the control of high-dimensional physically simulated humanoids. To do this, we propose a motor architecture that has the general structure of an inverse model with a latent-variable bottlenec...
| Автори: | , , , , , , , |
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| Формат: | Conference item |
| Опубліковано: |
2019
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