This paper looks at texture—middle-level components—as an important aspect of drawing. We present a hardware tool, penPal, that is designed to support dynamic mark-making and direct creative actions at this level. By incorporating a tendon-driven continuum robot, penPal’s tip can move independently, giving the user a new axis of creative control. Combined with the user’s own manipulations, penPal allows for emergent combinations of computer and manual control over the rapid generation of diverse textures. Through a 10-participant study and a professional artist commission, we examine how users negotiate control by integrating multiple coordinate systems (their body, the paper, and penPal’s tip) as they construct compositions. We suggest some benefits of supporting users at the texture level, such as the ability to shift the primary focus of their activity, the ability to selectively defamiliarize the creative process for generative potential, and for pleasure.

We distilled a set of core practices within “morphing matter” research, derived a set of underlying skills and values, and developed these into a weekend workshop for high-school students. Participants in our workshop sampled a variety of research processes, including materials science and contextual design, incorporating curriculum-appropriate learning goals, toward an integrated pneumatic fashion project. We describe our approach, activity plan, and assessment as well as opportunities for research as an educational template to push beyond current “STEAM”-based educational practices for cross-disciplinary engagement.

By documenting and annotating one author's ongoing project combining 3D printing and handweaving to produce computational hybrid baskets, we contribute a framework for understanding hybrid craft. We identify three levels of material practice as observed in the basketry project—physical joinery between rigid printed-plastic parts and soft textiles, seamful multipart fabrication workflows, and aesthetics which negotiate between “basketlike” and “computational” forms—and analyze tensions and possible resolutions at each level.

Computational mediation can unlock access to existing creative fabrication tools. By outfitting an otherwise purely mechanical hand-operated knitting machine with lightweight sensing capabilities, we produced a system which provides immediate feedback about the state and affordances of the underlying knitting machine. We describe our technical implementation, show modular interface applications which center the particular patterning capabilities of this kind of machine knitting, and discuss user experiences with interactive hybrid computational/mechanical systems.

We propose an approach to enabling exploratory creativity in digital fabrication through the use of grain spaces. In material processes, “grain” describes underlying physical properties like the orientation of cellulose fibers in wood that, in aggregate, affect fabrication concerns (such as directional cutting) and outcomes (such as axes of strength and visual effects). Extending this into the realm of computational fabrication, grain spaces define a curated set of mid-level material properties as well as the underlying low-level fabrication processes needed to produce them. We specify a grain space for computational brioche knitting, use it to guide our production of a set of hybrid digital/physical tools to support quick and playful exploration of this space’s unique design affordances, and reflect on the role of such tools in creative practice.

Knitting machines can fabricate complex fabric structures using robust industrial fabrication machines. However, machine knitting’s full capabilities are only available through low-level programming languages that operate on individual machine operations. We present KnitScript, a domain-specific machine knitting scripting language that supports computationally driven knitting designs. KnitScript provides a comprehensive virtual model of knitting machines, giving access to machine-level capabilities as they are needed while automating a variety of tedious and error-prone details. Programmers can extend KnitScript with Python programs to create more complex programs and user interfaces. We evaluate the expressivity of KnitScript through a user study where nine machine knitters used KnitScript code to modify knitting patterns. We demonstrate the capabilities of KnitScript through three demonstrations where we create: a program for generating knitted figures of randomized trees, a parameterized hat template that can be modified with accessibility features, and a pattern for a parametric mixed-material lampshade. KnitScript advances the state of machine-knitting research by providing a platform to develop and share complex knitting algorithms, design tools, and patterns. 1

The word “robot” frequently conjures unrealistic expectations of utilitarian perfection: tireless, efficient and flawless agents. However, real-world robots are far from perfect—they fail and make mistakes. Thus, roboticists should consider altering their current assumptions and cultivating new perspectives that account for a more complete range of robot roles, behaviors, and interactions. To encourage this, we explore the use of metaphors for generating novel ideas and reframing existing problems, eliciting new perspectives of human-robot interaction. Our work makes two contributions. We (1) surface current assumptions that accompany the term “robots,” and (2) present a collection of alternative perspectives of interaction with robots through metaphors. By identifying assumptions, we provide a comprehensible list of aspects to reconsider regarding robots’ physicality, roles, and behaviors. Through metaphors, we propose new ways of examining how we can use, relate to, and co-exist with the robots that will share our future.

Our work centers on aspects of crafting creativity that are often overlooked in digital fabrication: playful- ness, and possibilities for social engagement. We draw from precedents in both crafting (e.g. quilting bees) and gameplay (e.g. “Exquisite Corpse”) to inform the design of a set of turn-based collaborative games which center a computer-controlled embroidery machine as a “player” in games for one or more crafters. We proto- type these games using our own computational input/ output embroidery pipeline and observe how they can guide crafter-players to engage with physical, digital, and social affordances. We summarize our findings on how creative focus can shift over a playful experience of fabrication and how technology can mediate social crafting.

Machine knitting is an increasingly accessible fabrication technology for producing custom soft goods. However, recent machine knitting research has focused on knit shaping, or on adapting hand-knitting patterns. We explore a capability unique to machine knitting: producing multilayer spacer fabrics. These fabrics consist of two face layers connected by a monofilament filler yarn which gives the structure stiffness and volume. We show how to vary knit patterning and yarn parameters in spacer fabrics to produce tactile materials with embedded functionality for forming soft actuated mechanisms and sensors with tunable density, stiffness, material bias, and bristle properties. These soft mechanisms can be rapidly produced on a computationally-controlled v-bed knitting machine and integrated directly into soft objects.

We present an inexpensive tabletop loom that offers fully computational patterning while maintaining the flexibility of handweaving. Our loom can be assembled for under US$200 with 3D printed parts, and it can be controlled straightforwardly over USB. Our loom is explicitly a hand loom: that is, a weaver is required to operate the weaving process and may mediate row-by-row patterning and material specifics like yarn tension. Our approach combines the flexibility of fully analog handweaving with the computational affordances of digital fabrication: it enables the incorporation of special techniques and materials, as well as allowing for the possibility of computational and creative interventions in the weaving process itself. In taking this approach, we aim to serve a range of end users including artisans and researchers, whether for skill-building, for rapid prototyping, or for creative reflection.

Computational handweaving combines the repeatable precision of digital fabrication with relatively high production demands of the user: a weaver must be physically engaged with the system to enact a pattern, line by line, into a fabric. Rather than approaching co-presence and repetitive labor as a negative aspect of design, we look to current practices in procedural generation (most commonly used in game design and screen-based new media art) to understand how designers can create room for suprise and emergent phenomena within systems of precision and constraint. We developed three designs for blending real-time input with predetermined pattern features. These include: using camera imagery sampled at weaving time; a 1:1 scale tool for composing patterns on the loom; and a live "Twitch'' stream where spectators determine the woven pattern. We discuss how experiential qualities of the systems led to different balances of underdetermination in procedural generation as well as how such an approach might help us think beyond an artifact/experience dichotomy in fabrication.

With recent interest in shape-changing interfaces, material-driven design, wearable technologies, and soft robotics, digital fabrication of soft actuatable material is increasingly in demand. Much of this research focuses on elastomers or non-stretchy air bladders. Computationally-controlled machine knitting offers an alternative fabrication technology which can rapidly produce soft textile objects that have a very different character: breathable, lightweight, and pleasant to the touch. These machines are well established and optimized for the mass production of garments, but compared to other digital fabrication techniques such as CNC machining or 3D printing, they have received much less attention as general purpose fabrication devices. In this work, we explore new ways to employ machine knitting for the creation of actuated soft objects. We describe the basic operation of this type of machine, then show new techniques for knitting tendon-based actuation into objects. We explore a series of design strategies for integrating tendons with shaping and anisotropic texture design. Finally, we investigate different knit material properties, including considerations for motor control and sensing.

Knitting creates complex, soft fabrics with unique texture properties that can be used to create interactive objects.However, little work addresses the challenges of designing and using knitted textures computationally. We present KnitPick: a pipeline for interpreting hand-knitting texture patterns into KnitGraphs which can be output to machine and hand-knitting instructions. Using KnitPick, we contribute a measured and photographed data set of 472 knitted textures. Based on findings from this data set, we contribute two algorithms for manipulating KnitGraphs. KnitCarving shapes a graph while respecting a texture, and KnitPatching combines graphs with disparate textures while maintaining a consistent shape. KnitPick is the first system to bridge the gap between hand- and machine-knitting when creating complex knitted textures.

We present the first computational approach that can transform three-dimensional (3D) meshes, created by traditional modeling programs, directly into instructions for a computer-controlled knitting machine. Knitting machines are able to robustly and repeatably form knitted 3D surfaces from yarn but have many constraints on what they can fabricate. Given user-defined starting and ending points on an input mesh, our system incrementally builds a helix-free, quad-dominant mesh with uniform edge lengths, runs a tracing procedure over this mesh to generate a knitting path, and schedules the knitting instructions for this path in a way that is compatible with machine constraints. We demonstrate our approach on a wide range of 3D meshes.

Industrial knitting machines can produce finely detailed, seamless, 3D surfaces quickly and without human intervention. However, the tools used to program them require detailed manipulation and understanding of low-level knitting operations. We present a compiler that can automatically turn assemblies of high-level shape primitives (tubes, sheets) into low-level machine instructions. These high-level shape primitives allow knit objects to be scheduled, scaled, and otherwise shaped in ways that require thousands of edits to low-level instructions. At the core of our compiler is a heuristic transfer planning algorithm for knit cycles, which we prove is both sound and complete. This algorithm enables the translation of high-level shaping and scheduling operations into needle-level operations. We show a wide range of examples produced with our compiler and demonstrate a basic visual design interface that uses our compiler as a backend.