Regensburg 2025 – scientific programme
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SOE: Fachverband Physik sozio-ökonomischer Systeme
SOE 7: Focus Session: Self-Regulating and Learning Systems: from Neural to Social Networks
SOE 7.10: Talk
Wednesday, March 19, 2025, 12:30–12:45, H45
Neural self-organization of muscle-driven robots via force-mediated networks — •Claudius Gros1 and Bulcsu Sandor2 — 1Institute for Theoretical Physics, Goethe University Frankfurt — 2Department of Physics, Babes-Bolyai University, Cluj-Napoca, Romania
We present self-organizing control principles for simulated robots actuated by synthetic muscles. Muscles correspond to linear motors exerting force only when contracting, but not when expanding, with joints being actuated by pairs of antagonistic muscles. Individually, muscles are connected to a controller composed of a single neuron with a dynamical threshold that generates target positions for the respective muscle. A stable limit cycle is generated when the embodied feedback loop is closed, giving rise to regular locomotive patterns. In the absence of direct couplings between neurons, we show that the network generated by force-mediated intra- and inter-leg couplings between muscles suffice to generate stable gaits.
[1] Sándor, Bulcsú, and Claudius Gros. "Self-organized attractoring in locomoting animals and robots: an emerging field." International Conference on Artificial Neural Networks. Springer, 2024.
Keywords: self-organization; neurobots; force networks; attractoring