ReviewAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026
Vine-Inspired Soft Robotics: Materials, Actuations, and Emerging Applications.
Review in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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.
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.
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0 citing papers in PubMed.
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Authors and funding
8 authors.
Funding
Abstract
Inspired by the tip-growth behavior of natural vines, vine-inspired robots have emerged as a distinctive paradigm in soft robotics, enabling adaptive extension and navigation in confined, fragile, and highly unstructured environments. Unlike conventional robots that rely on whole-body locomotion, vine-inspired robots extend by generating or deploying their body from the tip, making performance strongly dependent on material properties, interfaces, and processing strategies. This review provides a comprehensive overview of recent advances from a materials perspective. We first examine material strategies for fluid-driven eversion and phase-transition- or deposition-enabled growth, linking material chemistry, geometry, interfaces, and phase behavior to growth performance. We then discuss how material-structure design supports steering, retraction, multifunctional tip design, variable stiffness, climbing and attachment, and branching, while evaluating their performance trade-offs and technological maturity. Representative applications in medical diagnosis and intervention, navigation and exploration, transport and operational tasks, and deployable and reconfigurable structures are highlighted. Finally, we discuss key challenges in hierarchical material integration, scalable manufacturability, long-term reliability, and morphology-aware sensing and control, together with opportunities enabled by learning-based approaches and embodied intelligence. By connecting materials, growth mechanisms, functional capabilities, and application requirements, this review provides a unified framework for next-generation adaptive growing robots.
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Registered trials
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.