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arXiv · 2609.27001

Humanoid Locomotion with a Fly-Inspired Recurrent Controller

Isabel Guan·Yuntian Zhao·Dingyuan Zhang·Shipeng Lyu
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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.

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Isabel Guan, Yuntian Zhao, Dingyuan Zhang, Shipeng Lyu. 2026-09-22. Humanoid Locomotion with a Fly-Inspired Recurrent Controller. https://arxiv.org/abs/2609.27001

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