Functional Fibers in Soft Robotics: Advances in Material, Structural, and Systemic Tactics

Joonhee Won, Jaehyun Jang, Hyeonseo Kim, Inyoung Choi, Steve Park, Seongjun Park · Wiley · 2026

This review highlights the role of functional fibers in advancing soft robotics through adaptive material properties and structural design.

High AI ConfidenceStrong SourceUnknownReadiness Unknown

Plain English summary

The article reviews the shift in soft robotics from traditional control methods to a more integrated approach where material properties and design work together. It categorizes fibrous soft robotic systems into three levels: material-level, structural, and systemic integration, each contributing to the development of intelligent robotic applications.

Why this matters

This research is significant as it addresses the challenges in soft robotics by leveraging the unique properties of functional fibers. By enhancing the interaction between materials and design, it paves the way for more advanced and adaptable robotic systems that could have wide-ranging applications in various fields.

Key findings

  • Functional fibers enable autonomous sensing and actuation.
  • Geometric architectures can program complex deformation modes.
  • Fiber-based systems can achieve high degrees of freedom in robotics.
  • Advanced fabrication techniques enhance the capabilities of soft robotic systems.
  • These innovations can lead to bio-inspired nervous systems and artificial muscles.

What's new

The review categorizes soft robotic systems into three tiers of embodiment, providing a structured understanding of how material properties and design can lead to adaptive behaviors.

Limitations

The abstract does not provide specific examples of applications or detailed results from the review.

Commercial context

The abstract does not mention any commercial applications or readiness.

Publication

Publisher
Wiley
Publication date
February 20, 2026
Research type
Article
License
http://creativecommons.org/licenses/by/4.0/

Tags

More on Programmable Materials

See all →
Programmable Materialspaper· Jul 1, 2026

Shape optimization of 4D-printed multi-material morphing structures for enhanced structural stability

This study presents a shape optimization framework for enhancing the stiffness of 4D-printed multi-material morphing structures.

Hoo Min Lee, Chang-Min Lee +2 · IOP PublishingWorking Prototype
Programmable Materialspaper· Jun 22, 2026

A Versatile‐Designable Framework for Active and Programmable Shape‐Morphing Soft Matter Systems: From Inverse Design to Closed‐Loop Control

This research presents a framework for active and programmable shape-morphing soft matter systems, enhancing soft robotics capabilities.

Kai Liu, Peiling Xie +4 · WileyLaboratory Research
Programmable Materialspaper· Jun 5, 2026

Architecting three-dimensional reconfigurable matter from pop-up kirigami with programmable multistability

This research presents a new platform for creating programmable multistable pop-up kirigami systems that can transform into complex 3D shapes.

Tong Zhou, Chong Huang +5 · American Association for the Advancement of Science (AAAS)Concept
Programmable Materialspreprint· May 2, 2026

Dimple-Encoded Reprogrammable Origami

This research presents a dimple-encoded origami platform that allows for reprogrammable shape-morphing and adaptive mechanical systems.

Qun Zhang, Weicheng Huang +5 · arXivLaboratory Research
Programmable Materialspreprint· Apr 30, 2026

Geometric memory in incomplete phase transitions across dimensions

A nucleation-and-growth model with incomplete reversion produces a geometric memory in plate-size distributions, with stronger memory in 2D than in 3D or lamellar geometries.

F. Tolea, M. Tolea · APS Open Sci. 1, 000005 (2026)Simulation
Programmable Materialspaper· Apr 24, 2026

On tailoring morphing mechanics of a bistable composite helical structure

A novel framework for tailoring the mechanics of bistable composite helical structures enhances their morphing capabilities for aerospace applications.

Biao Xu, Bing Wang +4 · SAGE PublicationsConcept
Method note: Summaries and ratings on this page are generated by AI from the abstract only. Read the original paper for full context. · Model: gpt-4o-mini-2024-07-18