Hierarchical visuomotor control of humanoids
November 23, 2018 Β· Declared Dead Β· π International Conference on Learning Representations
"No code URL or promise found in abstract"
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Authors
Josh Merel, Arun Ahuja, Vu Pham, Saran Tunyasuvunakool, Siqi Liu, Dhruva Tirumala, Nicolas Heess, Greg Wayne
arXiv ID
1811.09656
Category
cs.AI: Artificial Intelligence
Cross-listed
cs.RO
Citations
99
Venue
International Conference on Learning Representations
Last Checked
3 months ago
Abstract
We aim to build complex humanoid agents that integrate perception, motor control, and memory. In this work, we partly factor this problem into low-level motor control from proprioception and high-level coordination of the low-level skills informed by vision. We develop an architecture capable of surprisingly flexible, task-directed motor control of a relatively high-DoF humanoid body by combining pre-training of low-level motor controllers with a high-level, task-focused controller that switches among low-level sub-policies. The resulting system is able to control a physically-simulated humanoid body to solve tasks that require coupling visual perception from an unstabilized egocentric RGB camera during locomotion in the environment. For a supplementary video link, see https://youtu.be/7GISvfbykLE .
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