@tuthill.bsky.social @jan-ache.bsky.social @srinituraga.bsky.social @dddavi.bsky.social @bingbrunton.bsky.social

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Finally, I cannot end my words without thanking the open-source ecosystem that made this possible: OpenSim, MuJoCo, MyoSuite, and many more. Code links: • OpenSim optimization: github.com/gizemozd/neu... • MuJoCo imitation learning: github.com/gizemozd/Fly...

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Huge thanks to my co-first author Chuanfang Ning, whose master’s thesis sparked this project, and to all co-authors: Jasper S. Phelps, Sibo Wang-Chen (@wangchen.bsky.social), Guy Elisha, Alexander Blanke, Auke Ijspeert, Pavan Ramdya (@ramdya.bsky.social).

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While we only optimized front-leg muscles, we provide mid & hind-leg scaffolds so their parameters can be fit next using the same pipeline. Our model bridges motor neurons and joints, paving the way for plugging more realistic neural networks derived from the connectome into embodied agents.

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Muscles don’t act alone; passive joint properties matter too. We ported the model to MuJoCo (via MyoConverter) for large-scale, fast simulation and set up imitation learning with muscle-driven control. We found that passive properties in the joints stabilize control and speed up learning.

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