Unlocking fast robotic locomotor propulsion through dynamic spine-leg synergy

Quadrupeds in nature achieve agile locomotion through a rhythmic flexing of the spine in coordination with leg movement. This dynamic synergy enhances their speed and stability, reflecting a kind of physical intelligence encoded in their bodies. However, identifying this spine-leg synergy and embodying it in robotics to enhance locomotor propulsion remains challenging. To address this, we developed FLEXOR (fast legged robot with a flexible spine for optimal running), which uses dual-joint coupled spine and elastic legs to capture dynamic spine-leg synergy in rapid propulsion of small-scale quadrupeds. Through simulation and physical experiments of FLEXOR, we identified an optimal dynamic spine-leg synergy that maximizes locomotor capabilities by aligning the ground reaction force (GRF) for effective forward propulsion. Moreover, the dual-joint coupled spine amplifies the actuator torque, thereby increasing both the GRF magnitude and the propulsive output without additional energy input. By harnessing this synergy and its structural advantage, FLEXOR achieves a substantial increase in speed with a reduced cost of transport, outperforming state-of-the-art quadruped robots with flexible spines. Through an extended dynamics framework and robophysical validation, we further demonstrate that the optimal spine-leg synergy generalizes robustly across diverse spine-leg morphologies and physical scales. This work broadens our understanding of the essence of synergistic locomotion in animals and is potentially applicable for the design and control of embodied intelligence–driven robots.

Authors

Institutions

Publication Details

Journal
Science Advances
Published
2026-09-25
DOI
https://doi.org/10.1126/sciadv.aed5603
Primary Topic
Robotic Locomotion and Control
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

Unlocking fast robotic locomotor propulsion through dynamic spine-leg synergy

Zhiqiang Yu, Qing Shi, Zhenshan Bing, Gang Wang et al.
Science Advances
Robotic Locomotion and Control
article

Unlocking fast robotic locomotor propulsion through dynamic spine-leg synergy

Zhiqiang Yu, Qing Shi, Zhenshan Bing, Gang Wang, Rongjie Du, Ruochao Wang, Jian Sun, Xiaolong Quan, Weitao Zhang
article en

Abstract

Quadrupeds in nature achieve agile locomotion through a rhythmic flexing of the spine in coordination with leg movement. This dynamic synergy enhances their speed and stability, reflecting a kind of physical intelligence encoded in their bodies. However, identifying this spine-leg synergy and embodying it in robotics to enhance locomotor propulsion remains challenging. To address this, we developed FLEXOR (fast legged robot with a flexible spine for optimal running), which uses dual-joint coupled spine and elastic legs to capture dynamic spine-leg synergy in rapid propulsion of small-scale quadrupeds. Through simulation and physical experiments of FLEXOR, we identified an optimal dynamic spine-leg synergy that maximizes locomotor capabilities by aligning the ground reaction force (GRF) for effective forward propulsion. Moreover, the dual-joint coupled spine amplifies the actuator torque, thereby increasing both the GRF magnitude and the propulsive output without additional energy input. By harnessing this synergy and its structural advantage, FLEXOR achieves a substantial increase in speed with a reduced cost of transport, outperforming state-of-the-art quadruped robots with flexible spines. Through an extended dynamics framework and robophysical validation, we further demonstrate that the optimal spine-leg synergy generalizes robustly across diverse spine-leg morphologies and physical scales. This work broadens our understanding of the essence of synergistic locomotion in animals and is potentially applicable for the design and control of embodied intelligence–driven robots.

Science AdvancesVol. 12(39)
Beijing Institute of Technology (CN), Beijing Academy of Artificial Intelligence (CN), Technical University of Munich (DE)
Affordable and clean energy
Openalex Percentile: Top 21%
Robotic Locomotion and Control
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.