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Digital knitting machines provide a fast and efficient way to create garments, but commercial knitting tools are limited to predefined templates. While many knitting design tools help users create patterns from scratch, modifying existing patterns remains challenging. This paper introduces KnitA11y, a digital machine knitting pipeline that enables users to import hand-knitting patterns, add accessibility features, and fabricate them using machine knitting. We support modifications such as holes, pockets, and straps/handles, based on common accessible functional modifications identified in a survey of Ravelry.com. KnitA11y offers an interactive design interface that allows users to visualize patterns and customize the position and shape of modifications. We demonstrate KnitA11y’s capabilities through diverse examples, including a sensory-friendly scarf with a pocket, a hat with a hole for assistive devices, a sock with a pull handle, and a mitten with a pocket for heating pads to alleviate Raynaud’s symptoms. 

Jamming actuators have been proposed for many portable or wearable applications, yet the performance of these actuators will vary widely with fluidic leaks that degrade vacuum pressure and therefore maximum stiffness and stiffness over time. We investigate the power consumption and pressure in a series of leaky jamming actuators using four approaches: continuous jamming, jamming once, and re-jamming at regular intervals or if the pressure falls outside a specified range. We demonstrate the pressures and power consumptions of these approaches in a soft gripper and an active robotic elbow brace. We found that re-jamming when pressure fell below a target range reduced power consumption by more than a factor of 7.5 over continuous jamming while maintaining performance. These findings, and other efficient re-jamming approaches, will be crucial to jamming robots that can operate after damage and untethered for multiple hours. 

Digital knitting machines have the capability to reliably manufacture seamless, textured, and multi-material garments, but these capabilities are obscured by limiting CAD tools. Recent innovations in computational knitting build on emerging programming infrastructure that gives full access to the machine’s capabilities but requires an extensive understanding of machine operations and execution. In this paper, we contribute a critical missing piece of the knitting-machine programming pipeline–a program optimizer. Program optimization allows programmers to focus on developing novel algorithms that produce desired fabrics while deferring concerns of efficient machine operations to the optimizer. We present KODA, the Knit-program Optimization by Dependency Analysis method. KODA re-orders and reduces machine instructions to reduce knitting time, increase knitting reliability, and manage boilerplate operations that adjust the machine state. The result is a system that enables programmers to write readable and intuitive knitting algorithms while producing efficient and verified programs