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Preprint . 2025
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Article . 2026 . Peer-reviewed
Data sources: Crossref
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License: CC BY
Data sources: Datacite
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License: CC BY
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Article . 2026
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Electrofluidic fiber muscles

Authors: O. K. Afsar; G. Pupillo; G. Vitucci; W. Babatain; H. Ishii; V. Cacucciolo;

Electrofluidic fiber muscles

Abstract

Actuators are to robots what muscles are to humans. They enable motion and determine strength and dexterity. The fiber form factor makes skeletal muscles modular, scalable, and densely integrated (50% of human body weight). In contrast, servo motors that drive today’s robots lack the flexibility and modularity of muscle fibers, limiting integration and dexterity. Here, we report electrofluidic fiber muscles, soft artificial muscles for robotic applications with power density comparable to skeletal muscles (50 watts per kilogram), contraction strains of 20%, and response time of 0.3 second. These 2-millimeter-thick muscles comprise antagonistic fluidic actuators driven by electrohydrodynamic fiber pumps in a closed circuit. They require no external liquid reservoir and are electrically driven, untethered, and silent. We demonstrated that performance is increased by pre-pressurizing the muscles at an optimal bias pressure. Applying bias pressure allowed the antagonist actuator to act as a reservoir for the agonist, enabled 200% higher operating voltages by preventing cavitation, and leveraged the nonlinear pressure-stroke response of the actuators, increasing strain threefold at a given pump pressure. We characterized and modeled their dynamics, identifying optimal bias pressures. Electrofluidic muscles scale by simply bundling fibers. By selecting the ratio between pumps and actuators, we programmed their performance for different robotic tasks: a fast lever (180 millimeters per second) that launches objects in <0.3 second; a strong bundle that lifts 4 kilograms (200 times its weight) with a 30-millimeter stroke; a woven muscle that bends a robot arm by 40° and is compliant enough for a human handshake.

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    popularity
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    influence
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Top 10%
Average
Average
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