Humanoid Locomotion with a Fly-Inspired Recurrent Controller

Abstract:We investigate humanoid locomotion with a fly-inspired recurrent controller and identify the pathways supporting its deployed behavior. The controller couples 3,609 continuous neural states to a simulated Unitree G1 through body-observation projections, a motor-neuron-labelled readout, and joint servos. We formulate this neural-body feedback system and evaluate a fixed checkpoint across seven terrain instances, three speeds, and three initial yaw offsets. It completes 61/63 conditions under a survival-and-forward-progress criterion; a privileged reference completes 62/63. At nominal yaw, resetting the recurrent motor state before every policy call changes success from 19/21 to 0/21. Conversely, depth and upstream-state substitutions at 252 recorded states leave actions unchanged, with zero measured descending output throughout the intact rollouts. Recorded trajectories and state-matched images connect these findings to sustained movement, lateral drift, and termination events. The study characterizes an embodied recurrent control system whose tested locomotion is supported by direct body-and-command input and carried motor state, providing a concrete basis for subsequent comparisons of circuit structure and control resources.

19 pages: 8 main text, 1 references, 10 supplementary material; 5 main and 10 supplementary figures. Simulation study

Robotics (cs.RO)

arXiv:2609.27001 [cs.RO]

(or arXiv:2609.27001v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.27001 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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