aiqbench analysis
This research advances quadruped locomotion by explicitly modeling periodic joint trajectories using feedback-modulated Fourier series, offering a structured alternative to standard neural network policies that treat gait cycles implicitly. The approach successfully adapts harmonic frequencies and phases in real-time based on robot state feedback, demonstrating improved load-carrying capacity and speed on physical hardware like the Unitree Go2. By bridging symbolic frequency representations with learned control, this work provides a new framework for optimizing dynamic stability in legged robots without relying solely on black-box activation patterns.
Feedback-Modulated Harmonic Policies for Quadruped Locomotion
arXiv:2609.17946v1 Announce Type: new Abstract: Learned quadruped locomotion policies commonly map observations directly to joint-level actions, leaving the periodic structure of locomotion implicit in the policy. We investigate an alternative representation in which each joint trajectory is expressed as a command-conditioned Fourier series and modified online using feedback from the robot state. A context network generates the Fourier coefficients and the weights of a per-step feedback network, whose outputs adjust joint offsets, harmonic gains, frequency, and phase during execution. In simulation, we examine this explicit frequency structure alongside the hidden activations of an MLP policy that directly outputs joint targets. The harmonic waveforms change frequency and shape with commanded speed. Dynamic mode decomposition of selected MLP rollouts reveals dominant activation modes near the foot-height oscillation frequency and its second harmonic, showing periodic structure without an explicit Fourier generator. On a Unitree Go2, the simulation-trained harmonic controller records a provisional onboard-estimated peak speed of 3.67 meter per second and carries added loads up to 5.883 kilogram in separate trials.
Abstract:Learned quadruped locomotion policies commonly map observations directly to joint-level actions, leaving the periodic structure of locomotion implicit in the policy. We investigate an alternative representation in which each joint trajectory is expressed as a command-conditioned Fourier series and modified online using feedback from the robot state. A context network generates the Fourier coefficients and the weights of a per-step feedback network, whose outputs adjust joint offsets, harmonic gains, frequency, and phase during execution. In simulation, we examine this explicit frequency structure alongside the hidden activations of an MLP policy that directly outputs joint targets. The harmonic waveforms change frequency and shape with commanded speed. Dynamic mode decomposition of selected MLP rollouts reveals dominant activation modes near the foot-height oscillation frequency and its second harmonic, showing periodic structure without an explicit Fourier generator. On a Unitree Go2, the simulation-trained harmonic controller records a provisional onboard-estimated peak speed of 3.67 meter per second and carries added loads up to 5.883 kilogram in separate trials.
Robotics (cs.RO)
arXiv:2609.17946 [cs.RO]
(or arXiv:2609.17946v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.17946 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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