ArticleScience advances2026
Programmable robotic textile with closed-loop self-sensing and active actuation.
Article in Science advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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Authors and funding
13 authors.
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Abstract
Robotic textiles that autonomously sense biological and environmental signals and perform closed-loop adaptive actuation represent a new paradigm for intelligent human-machine interfaces in precision medicine and human augmentation. However, mechanical incompatibility of their constituent materials and architectures, unstable sensing during dynamic human-machine interaction, and high-voltage safety concerns significantly restrict their reliable applications. Herein, a low-voltage-driven untethered robotic textile interlacing all-in-one core-sheath liquid metal (LM)-liquid crystal elastomer (LCE) fibers with passive yarns is proposed, enabling stable closed-loop sensing-control-actuation within a unified programmable architecture by sensing preload, strain and temperature variations. Thereinto, the hollow LCE fiber acts as an efficient actuating element and compliant protective sheath, and the LM core simultaneously provides robust strain sensing and electrothermal stimulation. These untethered closed-loop robotic textiles feature high-force and voltage-tunable actuation and programmable morphing with adjustable porosity, offering significant potential for locomotion support and training, posture correction, compression therapy, thermal management, and electromagnetic-interference attenuation.
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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.