Evidence map›Paper›PMID 40739032›Full record

ArticleNature communications2025

Slip-actuated bionic tactile sensing system with dynamic DC generator integrated E-textile for dexterous robotic manipulation.

Vashin Gautham, Ashutosh Panpalia, Hamid Manouchehri, Krushang Khimjibhai Gabani, Vinoop Anil, Shakunthala Yerneni, Rohit Thakar, Aayush Nayyar, Mandar Anil Payare, Emily Jorgensen and 3 more

Abstract read
PubMed Full text at PMC Publisher
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 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

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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

13 authors.

Vashin GauthamDepartment of Mechanical and Aerospace Engineering, University at Buffalo, The State University of New York, Buffalo, NY, USA.ORCID http://orcid.org/0000-0002-5438-1430
Ashutosh PanpaliaDepartment of Mechanical and Aerospace Engineering, University at Buffalo, The State University of New York, Buffalo, NY, USA.
Hamid ManouchehriDepartment of Mechanical and Aerospace Engineering, University at Buffalo, The State University of New York, Buffalo, NY, USA.ORCID http://orcid.org/0009-0006-7617-8325
Krushang Khimjibhai GabaniDepartment of Mechanical and Aerospace Engineering, University at Buffalo, The State University of New York, Buffalo, NY, USA.ORCID http://orcid.org/0000-0001-5172-1703
Vinoop AnilDepartment of Mechanical and Aerospace Engineering, University at Buffalo, The State University of New York, Buffalo, NY, USA.
Shakunthala YerneniDepartment of Mechanical and Aerospace Engineering, University at Buffalo, The State University of New York, Buffalo, NY, USA.
Rohit ThakarDepartment of Mechanical and Aerospace Engineering, University at Buffalo, The State University of New York, Buffalo, NY, USA.
Aayush NayyarDepartment of Mechanical and Aerospace Engineering, University at Buffalo, The State University of New York, Buffalo, NY, USA.
Mandar Anil PayareDepartment of Mechanical and Aerospace Engineering, University at Buffalo, The State University of New York, Buffalo, NY, USA.
Emily JorgensenDepartment of Mechanical and Aerospace Engineering, University at Buffalo, The State University of New York, Buffalo, NY, USA.
Ruizhe YangPritzker School of Molecular Engineering, The University of Chicago, Chicago, IL, USA.
Ehsan EsfahaniDepartment of Mechanical and Aerospace Engineering, University at Buffalo, The State University of New York, Buffalo, NY, USA.ORCID http://orcid.org/0000-0001-5893-4664
Jun LiuDepartment of Mechanical and Aerospace Engineering, University at Buffalo, The State University of New York, Buffalo, NY, USA. jliu238@buffalo.edu.ORCID http://orcid.org/0000-0001-6951-8826

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Dexterous manipulation in robotics requires coordinated sensing, signal processing, and actuation for real-time, precise object control. Despite advances, the current artificial tactile sensory system lacks the proficiency of the human sensory system in detecting multidirectional forces and multimodal stimuli. To address this limitation, we present a bio-inspired "slip-actuated" tactile sensing system, incorporating dynamic direct-current generator into stretchable electronic textile. This self-powered bionic tactile sensing system operates in conjunction with a normal force sensor, paralleling the functions of human rapid-adapting and slow-adapting mechanoreceptors, respectively. Furthermore, we tailor and integrate the bionic tactile sensing system with robotic fingers, creating a bionic design that mimics human skin and skeleton with mechanoreceptors. By embedding this system into the feedback loop of robotic fingers, we are able to achieve fast slip and grasp monitoring, as well as effective object manipulation. Moreover, we perform quantitative analysis based on Hertzian contact mechanics to fundamentally understand the dependency of output on force and velocity in our sensor system. The results of this work provide an artificial tactile sensing mechanism for AI-driven smart robotics with human-inspired tactile sensing capabilities for future manufacturing, healthcare, and human-machine interaction.

Indexed as

BionicsRoboticsTextilesTouchEquipment DesignFingersHand StrengthHumansMechanoreceptors

Identifiers

PMID40739032
PMCPMC12310937

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.