Evidence map›Paper›PMID 38225274›Full record

ArticleNature communications2024

A retrofit sensing strategy for soft fluidic robots.

Shibo Zou, Sergio Picella, Jelle de Vries, Vera G Kortman, Aimée Sakes, Johannes T B Overvelde

Abstract read
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 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
–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

14 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Active Oscillations of Elastic Membranes Arising from the Coupling with a Catalytic Reaction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
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  3. Receptogenesis in a Vascularized Robotic Embodiment.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Article
  4. Article
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  6. Rotational 3D printing of active-passive filaments and lattices with programmable shape morphing.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  7. Article
  8. Review
  9. Article
  10. Embodying Control in Soft Multistable Robots from Morphofunctional Co-design.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Article
  11. Article
  12. Article
  13. Article
  14. 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

6 authors.

Shibo ZouAutonomous Matter Department, AMOLF, Amsterdam, 1098 XG, The Netherlands.
Sergio PicellaAutonomous Matter Department, AMOLF, Amsterdam, 1098 XG, The Netherlands.
Jelle de VriesAutonomous Matter Department, AMOLF, Amsterdam, 1098 XG, The Netherlands.
Vera G KortmanDepartment of Marine and Transport Technology, Delft University of Technology, Delft, 2628 CD, The Netherlands.ORCID http://orcid.org/0000-0002-2370-0013
Aimée SakesBio-Inspired Technology Group, Department of BioMechanical Engineering, Delft University of Technology, Delft, 2628 CD, The Netherlands.
Johannes T B OverveldeAutonomous Matter Department, AMOLF, Amsterdam, 1098 XG, The Netherlands. overvelde@amolf.nl.ORCID http://orcid.org/0000-0003-0253-0147

Funding

EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020) 948132
6 · The paper itself

Abstract

Soft robots are intrinsically capable of adapting to different environments by changing their shape in response to interaction forces. However, sensory feedback is still required for higher level decisions. Most sensing technologies integrate separate sensing elements in soft actuators, which presents a considerable challenge for both the fabrication and robustness of soft robots. Here we present a versatile sensing strategy that can be retrofitted to existing soft fluidic devices without the need for design changes. We achieve this by measuring the fluidic input that is required to activate a soft actuator during interaction with the environment, and relating this input to its deformed state. We demonstrate the versatility of our strategy by tactile sensing of the size, shape, surface roughness and stiffness of objects. We furthermore retrofit sensing to a range of existing pneumatic soft actuators and grippers. Finally, we show the robustness of our fluidic sensing strategy in closed-loop control of a soft gripper for sorting, fruit picking and ripeness detection. We conclude that as long as the interaction of the actuator with the environment results in a shape change of the interval volume, soft fluidic actuators require no embedded sensors and design modifications to implement useful sensing.

Identifiers

PMID38225274
PMCPMC10789869

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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.