Evidence map›Paper›PMID 40064944›Full record

ArticleNature communications2025

Soft robotic hand with tactile palm-finger coordination.

Ningbin Zhang, Jieji Ren, Yueshi Dong, Xinyu Yang, Rong Bian, Jinhao Li, Guoying Gu, Xiangyang Zhu

Abstract read
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

  1. Review
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  13. Millimeter-scale fluid-driven soft robots.National science review · 2025
    Article
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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

8 authors.

Ningbin Zhang *Robotics Institute, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Jieji Ren *Robotics Institute, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Yueshi DongRobotics Institute, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Xinyu YangRobotics Institute, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Rong BianRobotics Institute, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Jinhao LiRobotics Institute, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China.ORCID http://orcid.org/0009-0001-1535-3664
Guoying GuRobotics Institute, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China. guguoying@sjtu.edu.cn.ORCID http://orcid.org/0000-0002-7778-4523
Xiangyang ZhuRobotics Institute, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China. mexyzhu@sjtu.edu.cn.ORCID http://orcid.org/0000-0003-4914-6636

Funding

China Postdoctoral Science Foundation 2022M722085National Natural Science Foundation of China (National Science Foundation of China) 52025057National Natural Science Foundation of China (National Science Foundation of China) 91948302Science and Technology Commission of Shanghai Municipality (Shanghai Municipal Science and Technology Commission) 24511103400
6 · The paper itself

Abstract

Soft robotic hands with integrated sensing capabilities hold great potential for interactive operations. Previous work has typically focused on integrating sensors with fingers. The palm, as a large and crucial contact region providing mechanical support and sensory feedback, remains underexplored due to the currently limited sensing density and interaction with the fingers. Here, we develop a sensorized robotic hand that integrates a high-density tactile palm, dexterous soft fingers, and cooperative palm-finger interaction strategies. The palm features a compact visual-tactile design to capture delicate contact information. The soft fingers are designed as fiber-reinforced pneumatic actuators, each providing two-segment motions for multimodal grasping. These features enable extensive palm-finger interactions, offering mutual benefits such as improved grasping stability, automatic exquisite surface reconstruction, and accurate object classification. We also develop palm-finger feedback strategies to enable dynamic tasks, including planar object pickup, continuous flaw detection, and grasping pose adjustment. Furthermore, our development, augmented by artificial intelligence, shows improved potential for human-robot collaboration. Our results suggest the promise of fusing rich palm tactile sensing with soft dexterous fingers for advanced interactive robotic operations.

Indexed as

FingersHandRoboticsTouchArtificial IntelligenceEquipment DesignFeedback, SensoryHand StrengthHumans

Identifiers

PMID40064944
PMCPMC11894155

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.