Evidence map›Paper›PMID 41663370›Full record

ArticleNature communications2026

Robotic leaping enhanced by thrust-induced hypogravity, achieving precise, predictable, and extended jumps.

Zijie Sun, Jianguo Zhao, Yangmin Li, Long Teng

Abstract read
In one paragraph

Article in Nature communications, 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. Passive wheels on legged robots: a survey.Frontiers in robotics and AI · 2026
    Review
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

4 authors.

Zijie Sun *Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong.ORCID http://orcid.org/0000-0001-9772-327X
Jianguo Zhao *Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong.ORCID http://orcid.org/0000-0003-3149-080X
Yangmin LiDepartment of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong. yangmin.li@polyu.edu.hk.
Long TengDepartment of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong. eric-long.teng@polyu.edu.hk.ORCID http://orcid.org/0000-0003-4853-3255

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Robotic jumping research advances engineering and biomimicry frontiers, prioritizing range, precision, and predictability to navigate unstructured environments. Earth's gravity necessitates powerful actuators and lightweight bodies in robotic designs for maximal jump height. While many robots excel in statical environments, precise, predictable jumps in dynamic settings remain challenging. We realized this with a bipedal robot leveraging thrust-induced hypogravity, alongside dual regulation of aerial attitude and parabolic trajectory via thrust vectoring. Hypogravity multiplies leap range (max: 6.9 m) despite leg force saturation, enabling the robot to clear multi-level stairs, a 2.35-m-high wall, and 3-m-wide stream. Parabolic trajectory regulation allows leap distance precision/consistency surpassing existing thrust-assisted hybrids and leg-only jumpers. It enables pre-jump prediction of aerial/landing positions and timing, facilitating leaps in dynamic scenarios: through fast-moving windows (3.8 m/s), onto shifting, confined targets, and against wind disturbance. This research establishes extended range, precise, and predictable jumping through self-generated hypogravity and parabolic trajectory regulation.

Identifiers

PMID41663370
PMCPMC12996329

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.