Publications

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Preprints

Robust control for multi-legged elongate robots in noisy environments

Published in arXiv preprint, 2025

This paper presents robust control methods for multi-legged elongate robots operating in noisy environments.

Recommended citation: Chong, Baxi*, Juntao He*, Daniel Irvine, Tianyu Wang, Esteban Flores, Daniel Soto, Jianfeng Lin, Zhaochen Xu, et al. (2025). "Robust control for multi-legged elongate robots in noisy environments." arXiv preprint arXiv:2506.15788. *Equal contribution.
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Journal Articles

Probabilistic approach to feedback control enhances multi-legged locomotion on rugged landscapes

Published in IEEE Transactions on Robotics, 2025

This paper demonstrates that a lightweight, interpretable feedback controller—driven only by binary foot-contact sensing—can substantially improve multilegged locomotion on rugged, unstructured terrain. By introducing a probabilistic model that links terrain-induced contact disruption to locomotion speed, we derive a cycle-to-cycle control law that modulates vertical body undulation to recover effective ground contact. Extensive laboratory and outdoor experiments show consistent gains in both average speed and speed stability, highlighting a practical approach to robust field locomotion with minimal sensing and computation.

Recommended citation: He, Juntao, Baxi Chong, Jianfeng Lin, Zhaochen Xu, Hosain Bagheri, Esteban Flores, and Daniel I. Goldman. (2025). "Probabilistic approach to feedback control enhances multi-legged locomotion on rugged landscapes." IEEE Transactions on Robotics.
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Multilegged matter transport: A framework for locomotion on noisy landscapes

Published in Science, 2023

Locomotion over rugged terrain, whether human or robotic, generally requires extensive feedback to allow for adjustments in stride to compensate for cracks, inclines, or changes in surface composition. This ability typically requires a network of sensors to detect changes in terrain. Chong et al. show that an alternative approach requiring minimal environmental awareness can guarantee a successful arrival using information theory. The authors draw a parallel between having multiple, connected legs on the robot and having signal transmission protocols that minimize error in transmission—in this case, the “signal” being transmitted is the body of the robot. —Marc S. Lavine

Recommended citation: Chong, Baxi, Juntao He, Daniel Soto, Tianyu Wang, Daniel Irvine, Greg Blekherman, and Daniel I. Goldman. (2023). "Multilegged matter transport: A framework for locomotion on noisy landscapes." Science. 380(6644), 509-515.
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Self-propulsion via slipping: Frictional swimming in multilegged locomotors

Published in Proceedings of the National Academy of Sciences, 2023

Drag anisotropy is believed to be the critical principle which enables effective undulatory swimming in flowable media. Here, we show that undulatory locomotion with leg retraction/protraction can be recast as a fluid-like problem with the nonlinearities of foot–ground interactions leading to acquired drag anisotropy. In doing so, our framework allows for the comparison and cross-referencing of undulatory locomotion across diverse substrates. Further, from robophysical and biological experiments, we show that undulatory multilegged frictional swimming can be quantitatively described using a geometric model with low-dimensional centralized control framework. Our analysis not only facilitates the control of robust robot locomotion in complex terradynamic scenarios but also gives insight into neuromechanical control and the evolution of myriapod locomotion.

Recommended citation: Chong, Baxi, Juntao He, Shaohang Li, Emily Erickson, Kevin Diaz, Tianyu Wang, Daniel Soto, and Daniel I. Goldman. (2023). "Self-propulsion via slipping: Frictional swimming in multilegged locomotors." Proceedings of the National Academy of Sciences. 120(11), e2213698120.
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Conference Papers

Tactile sensing enables vertical obstacle negotiation for elongate many-legged robots

Published in Robotics: Science and Systems, 2025

We propose a tactile sensing and control framework that enables an elongated many-legged robot to perform rapid 3D behaviors, including climbing large obstacles in confined, unstructured environments. By fusing a tactile antenna for obstacle probing with binary foot-contact feedback, our controller adaptively regulates head pitch and vertical body undulation, achieving robust climbs up to five times the robot’s height in lab and outdoor trials.

Recommended citation: He, Juntao, Baxi Chong, Meatano Iaschi, Vincent R. Nienhusser, Sehoon Ha, and Daniel I. Goldman. (2025). "Tactile sensing enables vertical obstacle negotiation for elongate many-legged robots." Robotics: Science and Systems.
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