Evidence map›Paper›PMID 42789728›Full record

ArticleScience advances2026

Unlocking fast robotic locomotor propulsion through dynamic spine-leg synergy.

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

Abstract read
In one paragraph

Article in Science advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Ruochao WangSchool of Artificial Intelligence, Beijing Institute of Technology, Beijing, China.ORCID 0000-0003-0256-8399
Weitao ZhangKey Laboratory of Biomimetic Robots and Systems, Beijing Institute of Technology, Ministry of Education, Beijing, China.ORCID 0009-0001-6105-9642
Xiaolong QuanKey Laboratory of Biomimetic Robots and Systems, Beijing Institute of Technology, Ministry of Education, Beijing, China.
Rongjie DuKey Laboratory of Biomimetic Robots and Systems, Beijing Institute of Technology, Ministry of Education, Beijing, China.
Zhenshan BingSchool of Computation, Information and Technology, Technical University of Munich, Munich, Germany.ORCID 0000-0002-0896-2517
Gang WangState Key Laboratory of Autonomous Intelligent Unmanned Systems, Beijing, China.ORCID 0000-0002-7266-2412
Jian SunState Key Laboratory of Autonomous Intelligent Unmanned Systems, Beijing, China.
Zhiqiang YuYangtze Delta Region Academy of Beijing Institute of Technology, Jiaxing, China.ORCID 0000-0002-8461-6309
Qing ShiSchool of Artificial Intelligence, Beijing Institute of Technology, Beijing, China.ORCID 0000-0002-9914-7314

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

LocomotionRoboticsSpineAnimalsBiomechanical PhenomenaHumans

Identifiers

PMID42789728
PMCPMC13614408

What OpenQuestion holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.