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Neural probabilistic motor primitives for humanoid control

Abstract:

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 bottleneck. We show that it is possible to train this model entirely offline to compress thousands of expert policies and learn a motor primitive embedding space. The trained neural probabilistic motor prim...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Accepted Manuscript

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Institution:
University of Oxford
Oxford college:
St Peter's College
Role:
Author
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Publication date:
2019-05-06
Acceptance date:
2018-12-21
Pubs id:
pubs:949226
URN:
uri:6a02be08-a2b6-46b5-a5cf-0ee99a23c67a
UUID:
uuid:6a02be08-a2b6-46b5-a5cf-0ee99a23c67a
Local pid:
pubs:949226

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