Closing the Loop on the Poppy Humanoid: Bipedal Locomotion with Linear-Quadratic Control and Learned Cost Functions
arXiv:2608.26505v1 Announce Type: new Abstract: The Poppy Humanoid is an open-source, low-cost robot suitable for research and education in artificial intelligence. However, we are unaware of any published methodology that achieves reliable, unassisted bipedal locomotion on the standard Poppy hardware. This paper contributes a functional closed-loop walking controller for Poppy, based on the linear-quadratic regulator (LQR) framework for trajectory tracking. Starting with data collected from open-loop playback of a nominal walking trajectory, our proposed method learns a quadratic cost function for an LQR controller that substantially improves the reliability of the motion. The closed-loop controller is validated empirically, demonstrating statistically significant improvements in walking performance compared to open-loop trajectory playback.
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[Submitted on 27 Aug 2026]
Title:Closing the Loop on the Poppy Humanoid: Bipedal Locomotion with Linear-Quadratic Control and Learned Cost Functions
View a PDF of the paper titled Closing the Loop on the Poppy Humanoid: Bipedal Locomotion with Linear-Quadratic Control and Learned Cost Functions, by Xulin Chen and Borui He and Ruipeng Liu and Naveed Tahir and Zhenyu Gan and Garrett E. Katz
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Abstract:The Poppy Humanoid is an open-source, low-cost robot suitable for research and education in artificial intelligence. However, we are unaware of any published methodology that achieves reliable, unassisted bipedal locomotion on the standard Poppy hardware. This paper contributes a functional closed-loop walking controller for Poppy, based on the linear-quadratic regulator (LQR) framework for trajectory tracking. Starting with data collected from open-loop playback of a nominal walking trajectory, our proposed method learns a quadratic cost function for an LQR controller that substantially improves the reliability of the motion. The closed-loop controller is validated empirically, demonstrating statistically significant improvements in walking performance compared to open-loop trajectory playback.
Comments: Accepted by International Conference on the AI Revolution: Research, Ethics, and Society (AIR-RES 2026)
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2608.26505 [cs.RO]
(or arXiv:2608.26505v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2608.26505
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Xulin Chen [view email] [v1] Thu, 27 Aug 2026 01:03:45 UTC (2,619 KB)
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