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Soft robots adapt passively to complex environments due to their inherent compliance, allowing them to interact safely with fragile or irregular objects and traverse uneven terrain. The vast tunability and ubiquity of textiles has enabled new soft robotic capabilities, especially in the field of wearable robots, but existing textile processing techniques (e.g., cut‐and‐sew, thermal bonding) are limited in terms of rapid, additive, accessible, and waste‐free manufacturing. While 3D knitting has the potential to address these limitations, an incomplete understanding of the impact of structure and material on knit‐scale mechanical properties and macro‐scale device performance has precluded the widespread adoption of knitted robots. In this work, the roles of knit structure and yarn material properties on textile mechanics spanning three regimes–unfolding, geometric rearrangement, and yarn stretching–are elucidated and shown to be tailorable across unique knit architectures and yarn materials. Based on this understanding, 3D knit soft actuators for extension, contraction, and bending are constructed. Combining these actuation primitives enables the monolithic fabrication of entire soft grippers and robots in a single‐step additive manufacturing procedure suitable for a variety of applications. This approach represents a first step in seamlessly “printing” conformal, low‐cost, customizable textile‐based soft robots on‐demand.

 

Knitting interloops one-dimensional yarns into three-dimensional fabrics that exhibit behaviour beyond their constitutive materials. How extensibility and anisotropy emerge from the hierarchical organization of yarns into knitted fabrics has long been unresolved. We seek to unravel the mechanical roles of tensile mechanics, assembly and dynamics arising from the yarn level on fabric nonlinearity by developing a yarn-based dynamical model. This physically validated model captures the mechanical response of knitted fabrics, analogous to flexible metamaterials and biological fibre networks due to geometric nonlinearity within such hierarchical systems. Fabric anisotropy originates from observed yarn–yarn rearrangements during alignment dynamics and is topology-dependent. This yarn-based model also provides a design space of knitted fabrics to embed functionalities by varying geometric configuration and material property in instructed procedures compatible to machine manufacturing. Our hierarchical approach to build up a knitted fabric computationally modernizes an ancient craft and represents a first step towards mechanical programmability of knitted fabrics in wide engineering applications.

 

Textiles hold great promise as a soft yet durable material for building comfortable robotic wearables and assistive devices at low cost. Nevertheless, the development of smart wearables composed entirely of textiles has been hindered by the lack of a viable sheet-based logic architecture that can be implemented using conventional fabric materials and textile manufacturing processes. Here, we develop a fully textile platform for embedding pneumatic digital logic in wearable devices. Our logic-enabled textiles support combinational and sequential logic functions, onboard memory storage, user interaction, and direct interfacing with pneumatic actuators. In addition, they are designed to be lightweight, easily integrable into regular clothing, made using scalable fabrication techniques, and durable enough to withstand everyday use. We demonstrate a textile computer capable of input-driven digital logic for controlling untethered wearable robots that assist users with functional limitations. Our logic platform will facilitate the emergence of future wearables powered by embedded fluidic logic that fully leverage the innate advantages of their textile construction.