We present PileUp, a tufted pile e-textile sensing approach that offers unique affordances through the tactile expressiveness and richness of its continuous, threaded-volume construction. By integrating conductive yarns in looped or cut pile forms, PileUp transforms soft 3-dimensional textiles into multimodal sensors capable of detecting mechanical deformations such as pressure, bending, and strain, as well as environmental conditions like moisture. We propose a design space that outlines the relationships between texture, form factor, and sensing affordances of tufted textiles. We characterize electrical responses under compression, bending, and strain, reporting sensor behaviors. To demonstrate versatility, we present three application scenarios in which PileUp sensors are seamlessly integrated into soft fabrics: a meditation rug with multi-zone sensing, a fleece sleeve that detects arm motion, and a moisture-sensing wall art. Our results establish tufting as an accessible yet expressive fabrication method for creating integrated sensing textiles, distinguishing our work from traditional flat textile sensors.

Hand edema, defined as swelling of the hands caused by excess fluid accumulation, is a pervasive condition affecting a person’s range of motion and functional ability. However, treatment strategies remain limited to time-consuming manual massage by trained therapists, deterring a widely accessible approach. We present KnitDema, a robotic textile device that allows sequential compression from distal to proximal finger phalanges for mobilizing edema. We machine-knit the device and integrate small-scale actuators to envelop granular body locations such as fingers, catering to the shape of the hand. In addition, the device affords customizable compression levels through the enclosed fiber-like actuators. We characterize compression parameters and simulate the shunting of edema through a mock fluid system. Finally, we conduct a case study to evaluate the feasibility of the device, in which five hand edema patients assess KnitDema. Our study provides insights into the opportunities for robotic textiles to support personalized rehabilitation.

In this article, we present a mechanism and related path planning algorithm to construct light-duty barriers out of extruded, inflated tubes weaved around existing environmental features. Our extruded tubes are based on everted vine-robots and in this context, we present a new method to steer their growth. We characterize the mechanism in terms of accuracy resilience, and, towards their use as barriers, the ability of the tubes to withstand distributed loads. We further explore an algorithm which, given a feature map and the size and direction of the external load, can determine where and how to extrude the barrier. Finally, we showcase the potential of this method in an autonomously extruded two-layer wall weaved around three pipes. While preliminary, our work indicates that this method has potential for barrier construction in cluttered environments, e.g. shelters against wind or snow. Future work may show how to achieve tighter weaves, how to leverage weave friction for improved strength, how to assess barrier performance for feedback control, and how to operate the extrusion mechanism off of a mobile robot.

We present KnitSkin, a bio-inspired sleeve that can traverse diverse cylindrical terrains, ranging from a user’s forearm at a wearable scale, to pipes and tree branches at an environmental scale. Fabricated with a machine knitted substrate, the sleeve configures a stepped array of knitted scales that exhibit anisotropic friction. Coupled with the extension of actuators enclosed in the sleeve, the scales enable effective directional locomotion on cylindrical surfaces with varying slopes, textures, and curvatures. KnitSkin’s substrates are characterized by scales whose geometries and materials can be fine-tuned and channels that can accommodate diverse actuators. We introduce the design elements of KnitSkin in which we characterize a series of substrate parameters and their resulting anisotropic behaviors. In evaluating the locomotion, we examine the variables associated with the surface and actuator characteristics. KnitSkin obtains diverse applications across different scales, including wearable interfaces, industrial pipe-monitoring, to agricultural robots.

We present KnitDermis, on-body interfaces that deliver expressive non-vibrating mechanotactile feedback on the wearer’s body. Fabricated through machine knitting, they embed shape-memory alloy micro-springs in knitted channels, which deliver tactile sensations on the skin when activated. KnitDermis interfaces take advantage of machine knitting’s shaping properties which allow it to generate slim, stretchable, and versatile forms that can conform to underexplored body locations, such as protruded joints and convex body locations. We introduce a fabrication approach and a series of case studies to design a wide range of form factors, textures, and tactile patterns, including compression, pinching, brushing, and twisting. We conduct a user study to elicit KnitDermis’ effectiveness and wearability on diverse body locations and engage users to unpack envisioned use cases and perceptions towards the interfaces. We draw insights from our extensive research-through-design investigations on the potential of knitting as a soft approach for close-body and expressive tactile interfaces.