Evidence map›Paper›PMID 41790937›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2026

Agile legged locomotion in reconfigurable modular robots.

Chen Yu, David Matthews, Jingxian Wang, Jing Gu, Douglas Blackiston, Michael Rubenstein, Sam Kriegman

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Agile legged locomotion in reconfigurable modular robots.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
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

7 authors.

Chen Yu *Center for Robotics and Biosystems, Northwestern University, Evanston, IL 60208.
David Matthews *Center for Robotics and Biosystems, Northwestern University, Evanston, IL 60208.ORCID 0000-0002-5626-4668
Jingxian Wang *Center for Robotics and Biosystems, Northwestern University, Evanston, IL 60208.ORCID 0000-0003-4904-8892
Jing GuCenter for Robotics and Biosystems, Northwestern University, Evanston, IL 60208.
Douglas BlackistonDepartment of Biology, Tufts University, Medford, MA 02155.ORCID 0000-0001-7231-0196
Michael RubensteinCenter for Robotics and Biosystems, Northwestern University, Evanston, IL 60208.ORCID 0000-0002-1289-4211
Sam KriegmanCenter for Robotics and Biosystems, Northwestern University, Evanston, IL 60208.ORCID 0000-0002-9207-1775

Funding

NSF (NSF) 2331581NSF (NSF) 2440412Schmidt Sciences G-22-64506
6 · The paper itself

Abstract

Legged machines are becoming increasingly agile and adaptive, but they have so far lacked the morphological diversity of legged animals, which have been rearranged and reshaped to fill millions of niches. Unlike their biological counterparts, legged machines have largely converged over the past decade to canonical quadrupedal and bipedal architectures that cannot be easily reconfigured to meet new tasks or recover from injury. Here, we introduce autonomous modular legs: agile yet minimal, single-degree-of-freedom jointed links that can learn complex dynamic behaviors and may be freely attached to form multilegged machines at the meter scale. This enables rapid repair, redesign, and recombination of highly dynamic modular agents that move quickly and acrobatically (nonquasistatically) through unstructured environments. Because each module is itself a complete agent, the bodies that contain them can sustain deep structural damage that would completely disable other legged robots. We also show how to encode the vast space of possible body configurations into a compact latent design space that can be efficiently explored, revealing a wide diversity of novel legged forms.

Indexed as

LocomotionRoboticsAnimalsAutonomous RobotsMachine Learningmachine learningmodularitymorphology

Identifiers

PMID41790937
PMCPMC12974426

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.