Evidence map›Paper›PMID 39496596›Full record

ArticleNature communications2024

Capturing forceful interaction with deformable objects using a deep learning-powered stretchable tactile array.

Chunpeng Jiang, Wenqiang Xu, Yutong Li, Zhenjun Yu, Longchun Wang, Xiaotong Hu, Zhengyi Xie, Qingkun Liu, Bin Yang, Xiaolin Wang and 6 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature communications, 2024. 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.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Review
  6. Review
  7. A framework for quantifying the mechanics of dexterous grasp.bioRxiv : the preprint server for biology · 2026
    Article
  8. Article
  9. Article
  10. 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

16 authors.

Chunpeng Jiang *National Key Laboratory of Advanced Micro and Nano Manufacture Technology, Shanghai Jiao Tong University, Shanghai, China.ORCID http://orcid.org/0000-0003-1171-4044
Wenqiang Xu *School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Yutong LiSchool of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Zhenjun YuSchool of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.ORCID http://orcid.org/0000-0001-9975-1653
Longchun WangNational Key Laboratory of Advanced Micro and Nano Manufacture Technology, Shanghai Jiao Tong University, Shanghai, China.
Xiaotong HuNational Key Laboratory of Advanced Micro and Nano Manufacture Technology, Shanghai Jiao Tong University, Shanghai, China.
Zhengyi XieNational Key Laboratory of Advanced Micro and Nano Manufacture Technology, Shanghai Jiao Tong University, Shanghai, China.
Qingkun LiuNational Key Laboratory of Advanced Micro and Nano Manufacture Technology, Shanghai Jiao Tong University, Shanghai, China.ORCID http://orcid.org/0000-0002-1522-5347
Bin YangNational Key Laboratory of Advanced Micro and Nano Manufacture Technology, Shanghai Jiao Tong University, Shanghai, China.
Xiaolin WangNational Key Laboratory of Advanced Micro and Nano Manufacture Technology, Shanghai Jiao Tong University, Shanghai, China.
Wenxin DuSchool of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Tutian TangSchool of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.ORCID http://orcid.org/0000-0001-8254-2038
Dongzhe ZhengSchool of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Siqiong YaoSJTU-Yale Joint Center of Biostatistics and Data Science, National Center for Translational Medicine, MoE, Key Lab of Artificial Intelligence, AI Institute Shanghai Jiao Tong University, Shanghai, China.
Cewu LuSchool of Artificial Intelligence, Shanghai Jiao Tong University, Shanghai, China. lucewu@sjtu.edu.cn.
Jingquan LiuNational Key Laboratory of Advanced Micro and Nano Manufacture Technology, Shanghai Jiao Tong University, Shanghai, China. jqliu@sjtu.edu.cn.ORCID http://orcid.org/0000-0003-4140-1516

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Capturing forceful interaction with deformable objects during manipulation benefits applications like virtual reality, telemedicine, and robotics. Replicating full hand-object states with complete geometry is challenging because of the occluded object deformations. Here, we report a visual-tactile recording and tracking system for manipulation featuring a stretchable tactile glove with 1152 force-sensing channels and a visual-tactile joint learning framework to estimate dynamic hand-object states during manipulation. To overcome the strain interference caused by contact with deformable objects, an active suppression method based on symmetric response detection and adaptive calibration is proposed and achieves 97.6% accuracy in force measurement, contributing to an improvement of 45.3%. The learning framework processes the visual-tactile sequence and reconstructs hand-object states. We experiment on 24 objects from 6 categories including both deformable and rigid ones with an average reconstruction error of 1.8 cm for all sequences, demonstrating a universal ability to replicate human knowledge in manipulating objects with varying degrees of deformability.

Identifiers

PMID39496596
PMCPMC11535439

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Registered trials

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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.