Hierarchical motor control in mammals and machines

Josh Merel (), Matthew Botvinick and Greg Wayne
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Josh Merel: DeepMind
Matthew Botvinick: DeepMind
Greg Wayne: DeepMind

Nature Communications, 2019, vol. 10, issue 1, 1-12

Abstract: Abstract Advances in artificial intelligence are stimulating interest in neuroscience. However, most attention is given to discrete tasks with simple action spaces, such as board games and classic video games. Less discussed in neuroscience are parallel advances in “synthetic motor control”. While motor neuroscience has recently focused on optimization of single, simple movements, AI has progressed to the generation of rich, diverse motor behaviors across multiple tasks, at humanoid scale. It is becoming clear that specific, well-motivated hierarchical design elements repeatedly arise when engineering these flexible control systems. We review these core principles of hierarchical control, relate them to hierarchy in the nervous system, and highlight research themes that we anticipate will be critical in solving challenges at this disciplinary intersection.

Date: 2019
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DOI: 10.1038/s41467-019-13239-6

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