Humans are able to convey different messages using only touch. Equipping robots with the ability to understand social touch adds another modality in which humans and robots can communicate. In this paper, we present a social gesture recognition system using a fabric-based, large-scale tactile sensor placed onto the arms of a humanoid robot. We built a social gesture dataset using multiple participants and extracted temporal features for classification. By collecting tactile data on a humanoid robot, our system provides insights into human-robot social touch, and displays that the use of fabric based sensors could be a potential way of advancing the development of spHRI systems for more natural and effective communication.

We introduce a novel method for fabricating functional flat-to-shape objects using a large computer-controlled sewing machine (11 ft / 3.4m wide), a process that is both rapid and scalable beyond the machine’s sewable area. Flat-to-shape deployable objects can allow for quick and easy need-based activation, but the selective flexibility required can involve complex fabrication or tedious assembly. In our method, we sandwich rigid form-defining materials, such as plywood and acrylic, between layers of fabric. The sewing process secures these layers together, creating soft hinges between the rigid inserts which allow the object to transition smoothly into its three-dimensional functional form with little post-processing.

We present an algorithm that canonicalizes the algebraic representations of the topological semantics of machine knitting programs. Machine knitting is a staple technology of modern textile production where hundreds of mechanical needles are manipulated to form yarn into interlocking loop structures. Our semantics are defined using a variant of a monoidal category, and they closely correspond to string diagrams. We formulate our canonicalization as an Abstract Rewriting System (ARS) over words in our category, and prove that our algorithm is correct and runs in polynomial time.

Both engineering kits and collaboration skills are increasingly prevalent in education and are often used in conjunction, yet little research addresses how kit hardware can be designed to support collaboration. Collaboration skills are best acquired through carefully designed collaborative experiences. While prior work highlights technology’s important role in fostering collaboration, less research explicitly explores hardware design. This paper posits that certain design features of kits can promote or hinder collaboration. We present a user study evaluating the collaborative assembly of two loom kits: one for higher education (RoboLoom) and one for individuals (Ashford Loom). We developed a coding scheme from the 3Cs framework (coordination, cooperation, and communication) to analyze which hardware features influenced collaboration. We find five design feature categories that may influence collaboration: repetitiveness, specificity, difficulty, parallelizability, and physicality. This paper presents our findings and recommendations for implementing these features into educational kit hardware design to create opportunities for collaboration.

Prior robot painting and drawing work, such as FRIDA, has focused on decreasing the sim-to-real gap and expanding input modalities for users, but the interaction with these systems generally exists only in the input stages. To support interactive, human-robot collaborative painting, we introduce the Collaborative FRIDA (CoFRIDA) robot painting framework, which can co-paint by modifying and engaging with content already painted by a human collaborator. To improve text-image alignment, FRIDA's major weakness, our system uses pre-trained text-to-image models; however, pre-trained models in the context of real-world co-painting do not perform well because they (1) do not understand the constraints and abilities of the robot and (2) cannot perform co-painting without making unrealistic edits to the canvas and overwriting content. We propose a self-supervised fine-tuning procedure that can tackle both issues, allowing the use of pre-trained state-of-the-art text-image alignment models with robots to enable co-painting in the physical world. Our open-source approach, CoFRIDA, creates paintings and drawings that match the input text prompt more clearly than FRIDA, both from a blank canvas and one with human created work. More generally, our fine-tuning procedure successfully encodes the robot's constraints and abilities into a foundation model, showcasing promising results as an effective method for reducing sim-to-real gaps.

Advances in artificial intelligence (AI) have enabled unprecedented capabilities, yet innovation teams struggle when envisioning AI concepts. Data science teams think of innovations users do not want, while domain experts think of innovations that cannot be built. A lack of effective ideation seems to be a breakdown point. How might multidisciplinary teams identify buildable and desirable use cases? This paper presents a first hand account of ideating AI concepts to improve critical care medicine. As a team of data scientists, clinicians, and HCI researchers, we conducted a series of design workshops to explore more effective approaches to AI concept ideation and problem formulation. We detail our process, the challenges we encountered, and practices and artifacts that proved effective. We discuss the research implications for improved collaboration and stakeholder engagement, and discuss the role HCI might play in reducing the high failure rate experienced in AI innovation.

Mosquitoes and other biting arthropods transmit diseases worldwide, causing over 700,000 deaths each year, and costing about 3 billion USD annually for Aedes species alone. Insect vectored diseases also pose a considerable threat to agricultural animals. While clothing could provide a simple solution to vector-borne diseases, modern textiles do not effectively block mosquito bites. Here we have designed three micro-resolution knitted structures, with five adjustable parameters that can block mosquito bites. These designs, which exhibit significant bite reduction were integrated into a computer numerical control knitting robot for mass production of bite-blocking garments with minimal human labor. We then quantified the comfort of blocking garments. Our knits enable individuals to protect themselves from insects amidst their day-to-day activities without impacting the environment.

We introduce solid knitting, a new fabrication technique that combines the layer-by-layer volumetric approach of 3D printing with the topologically-entwined stitch structure of knitting to produce solid 3D objects. We define the basic building blocks of solid knitting and demonstrate a working prototype of a solid knitting machine controlled by a low-level instruction language, along with a volumetric design tool for creating machine-knittable patterns.

We introduce solid knitting, a new fabrication technique that combines the layer-by-layer volumetric approach of 3D printing with the topologically-entwined stitch structure of knitting to produce solid 3D objects. We define the basic building blocks of solid knitting and demonstrate a working prototype of a solid knitting machine controlled by a low-level instruction language, along with a volumetric design tool for creating machine-knittable patterns. Solid knitting uses a course-wale-layer structure, where every loop in a solid-knit object passes through both a loop from the previous layer and a loop from the previous course. Our machine uses two beds of latch needles to create stitches like a conventional V-bed knitting machine, but augments these needles with a pair of rotating hook arrays to provide storage locations for all of the loops in one layer of the object. It can autonomously produce solid-knit prisms of arbitrary length, although it requires manual intervention to cast on the first layer and bind off the final row. Our design tool allows users to create solid knitting patterns by connecting elementary stitches; objects designed in our interface can---after basic topological checks and constraint propagation---be exported as a sequence of instructions for fabrication on the solid knitting machine. We validate our solid knitting hardware and software on prism examples, detail the mechanical errors which we have encountered, and discuss potential extensions to the capability of our solid knitting machine.

Programming low-level controls for knitting machines is a meticulous, timeconsuming task that demands specialized expertise. Recently, there has been a shift towards automatically generating low-level knitting machine programs from high-level knit representations that describe knit objects in a more intuitive, user-friendly way. Current high-level systems trade off expressivity for ease-of-use, requiring ad-hoc trapdoors to access the full space of machine capabilities, or eschewing completeness in the name of utility. Thus, advanced techniques either require ad-hoc extensions from domain experts, or are entirely unsupported. Furthermore, errors may emerge during the compilation from knit object representations to machine instructions. While the generated program may describe a valid machine control sequence, the fabricated object is topologically different from the specified input, with little recourse for understanding and fixing the issue. To address these limitations, we introduce instruction graphs, an intermediate representation capable of capturing the full range of machine knitting programs. We define a semantic mapping from instruction graphs to fenced tangles, which make them compatible with the established formal semantics for machine knitting instructions. We establish a semantics-preserving bijection between machine knittable instruction graphs and knit programs that proves three properties - upward, forward, and ordered (UFO) - are both necessary and sufficient to ensure the existence of a machine knitting program that can fabricate the fenced tangle denoted by the graph. As a proof-of-concept, we implement an instruction graph editor and compiler that allows a user to transform an instruction graph into UFO presentation and then compile it to a machine program, all while maintaining semantic equivalence. In addition, we use the UFO properties to more precisely characterize the limitations of existing compilers. This work lays the groundwork for more expressive and reliable automated knitting machine programming systems by providing a formal characterization of machine knittability.

Clinical practice guidelines, care pathways, and protocols are designed to support evidence-based practices for clinicians; however, their adoption remains a challenge. We set out to investigate why clinicians deviate from the "Wake Up and Breathe" protocol, an evidence-based guideline for liberating patients from mechanical ventilation in the intensive care unit (ICU). We conducted over 40 hours of direct observations of live clinical workflows, 17 interviews with frontline care providers, and 4 co-design workshops at three different medical intensive care units. Our findings indicate that unlike prior literature suggests, disagreement with the protocol is not a substantial barrier to adoption. Instead, the uncertainty surrounding the application of the protocol for individual patients leads clinicians to deprioritize adoption in favor of tasks where they have high certainty. Reflecting on these insights, we identify opportunities for technical systems to help clinicians in effectively executing the protocol and discuss future directions for HCI research to support the integration of protocols into clinical practice in complex, team-based healthcare settings.

Weaving is a fabrication process that is grounded in mathematics and engineering: from the binary, matrix-like nature of the pattern drafts weavers have used for centuries, to the punch card programming of the first Jacquard looms. This intersection of disciplines provides an opportunity to ground abstract mathematical concepts in a concrete and embodied art, viewing this textile art through the lens of engineering. Currently, available looms are not optimized to take advantage of this opportunity to increase mathematics learning by providing hands-on interdisciplinary learning in collegiate classrooms. In this work, we present SPEERLoom: an open-source, robotic Jacquard loom kit designed to be a tool for interweaving cloth fabrication, mathematics, and engineering to support interdisciplinary learning in the classroom. We discuss the design requirements and subsequent design of SPEERLoom. We also present the results of a pilot study in a post-secondary class finding that SPEERLoom supports hands-on, interdisciplinary learning of math, engineering, and textiles.

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.

Advanced Functional MaterialsVolume 33, Issue 26 2370160 FrontispieceFree Access 3D Knitting for Pneumatic Soft Robotics (Adv. Funct. Mater. 26/2023) Vanessa Sanchez, Vanessa Sanchez John A. Paulson School of Engineering and Applied Sciences, Harvard University, 29 Oxford St., Cambridge, MA, 02138 USA Wyss Institute for Biologically Inspired Engineering, 3 Blackfan Cir., Boston, MA, 02115 USA Department of Chemical Engineering, Stanford University, 443 Via Ortega, Stanford, CA, 94305 USASearch for more papers by this authorKausalya Mahadevan, Kausalya Mahadevan John A. Paulson School of Engineering and Applied Sciences, Harvard University, 29 Oxford St., Cambridge, MA, 02138 USASearch for more papers by this authorGabrielle Ohlson, Gabrielle Ohlson John A. Paulson School of Engineering and Applied Sciences, Harvard University, 29 Oxford St., Cambridge, MA, 02138 USASearch for more papers by this authorMoritz A. Graule, Moritz A. Graule John A. Paulson School of Engineering and Applied Sciences, Harvard University, 29 Oxford St., Cambridge, MA, 02138 USASearch for more papers by this authorMichelle C. Yuen, Michelle C. Yuen John A. Paulson School of Engineering and Applied Sciences, Harvard University, 29 Oxford St., Cambridge, MA, 02138 USASearch for more papers by this authorClark B. Teeple, Clark B. Teeple John A. Paulson School of Engineering and Applied Sciences, Harvard University, 29 Oxford St., Cambridge, MA, 02138 USASearch for more papers by this authorJames C. Weaver, James C. Weaver John A. Paulson School of Engineering and Applied Sciences, Harvard University, 29 Oxford St., Cambridge, MA, 02138 USA Wyss Institute for Biologically Inspired Engineering, 3 Blackfan Cir., Boston, MA, 02115 USASearch for more papers by this authorJames McCann, James McCann Robotics Institute, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA, 15213 USASearch for more papers by this authorKatia Bertoldi, Katia Bertoldi John A. Paulson School of Engineering and Applied Sciences, Harvard University, 29 Oxford St., Cambridge, MA, 02138 USASearch for more papers by this authorRobert J. Wood, Robert J. Wood John A. Paulson School of Engineering and Applied Sciences, Harvard University, 29 Oxford St., Cambridge, MA, 02138 USA Wyss Institute for Biologically Inspired Engineering, 3 Blackfan Cir., Boston, MA, 02115 USASearch for more papers by this author Vanessa Sanchez, Vanessa Sanchez John A. Paulson School of Engineering and Applied Sciences, Harvard University, 29 Oxford St., Cambridge, MA, 02138 USA Wyss Institute for Biologically Inspired Engineering, 3 Blackfan Cir., Boston, MA, 02115 USA Department of Chemical Engineering, Stanford University, 443 Via Ortega, Stanford, CA, 94305 USASearch for more papers by this authorKausalya Mahadevan, Kausalya Mahadevan John A. Paulson School of Engineering and Applied Sciences, Harvard University, 29 Oxford St., Cambridge, MA, 02138 USASearch for more papers by this authorGabrielle Ohlson, Gabrielle Ohlson John A. Paulson School of Engineering and Applied Sciences, Harvard University, 29 Oxford St., Cambridge, MA, 02138 USASearch for more papers by this authorMoritz A. Graule, Moritz A. Graule John A. Paulson School of Engineering and Applied Sciences, Harvard University, 29 Oxford St., Cambridge, MA, 02138 USASearch for more papers by this authorMichelle C. Yuen, Michelle C. Yuen John A. Paulson School of Engineering and Applied Sciences, Harvard University, 29 Oxford St., Cambridge, MA, 02138 USASearch for more papers by this authorClark B. Teeple, Clark B. Teeple John A. Paulson School of Engineering and Applied Sciences, Harvard University, 29 Oxford St., Cambridge, MA, 02138 USASearch for more papers by this authorJames C. Weaver, James C. Weaver John A. Paulson School of Engineering and Applied Sciences, Harvard University, 29 Oxford St., Cambridge, MA, 02138 USA Wyss Institute for Biologically Inspired Engineering, 3 Blackfan Cir., Boston, MA, 02115 USASearch for more papers by this authorJames McCann, James McCann Robotics Institute, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA, 15213 USASearch for more papers by this authorKatia Bertoldi, Katia Bertoldi John A. Paulson School of Engineering and Applied Sciences, Harvard University, 29 Oxford St., Cambridge, MA, 02138 USASearch for more papers by this authorRobert J. Wood, Robert J. Wood John A. Paulson School of Engineering and Applied Sciences, Harvard University, 29 Oxford St., Cambridge, MA, 02138 USA Wyss Institute for Biologically Inspired Engineering, 3 Blackfan Cir., Boston, MA, 02115 USASearch for more papers by this author First published: 27 June 2023 https://doi.org/10.1002/adfm.202370160AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat Graphical Abstract Soft Robotics In article number 2212541, Robert J. Wood, Vanessa Sanchez, and co-workers demonstrate additively manufactured soft robots formed through 3D knitting. The variety of structures generated via machine knitting enables complex actuation motions from as little as one yarn and supports multi-actuator devices with integrated sensing. Volume33, Issue26June 26, 20232370160 RelatedInformation

Advances in artificial intelligence have enabled unprecedented technical capabilities, yet making these advances useful in the real world remains challenging. We engaged in a Research through Design process to improve the ideation of AI products and services. We developed a design resource capturing AI capabilities based on 40 AI features commonly used across various domains. To probe its usefulness, we created a set of slides illustrating AI capabilities and asked designers to ideate AI-enabled user experiences. We also incorporated capabilities into our own design process to brainstorm concepts with domain experts and data scientists. Our research revealed that designers should focus on innovations where moderate AI performance creates value. We reflect on our process and discuss research implications for creating and assessing resources to systematically explore AI's problem-solution space.

Tactile sensing is essential for robots to perceive and react to the environment. However, it remains a challenge to make large-scale and flexible tactile skins on robots. Industrial machine knitting provides solutions to manufacture customizable fabrics. Along with functional yarns, it can produce highly customizable circuits that can be made into tactile skins for robots. In this work, we present RobotSweater, a machine-knitted pressure-sensitive tactile skin that can be easily applied on robots. We design and fabricate a parameterized multi-layer tactile skin using off-the-shelf yarns, and characterize our sensor on both a flat testbed and a curved surface to show its robust contact detection, multi-contact localization, and pressure sensing capabilities. The sensor is fabricated using a well-established textile manufacturing process with a programmable industrial knitting machine, which makes it highly customizable and low-cost. The textile nature of the sensor also makes it easily fit curved surfaces of different robots and have a friendly appearance. Using our tactile skins, we conduct closed-loop control with tactile feedback for two applications: (1) human lead-through control of a robot arm, and (2) human-robot interaction with a mobile robot.

We show that a linear model is sufficient to accurately estimate the quantity of yarn that goes into a knitted item produced on an automated knitting machine. Knitted fabrics are complex structures, yet their diverse properties arise from the arrangement of a small number of discrete, additive operations. One can estimate the masses of each of these basic yarn additions using linear regression and, in turn, use these masses to estimate the overall quantity (and local distribution) of yarn within any knitted fabric. Our proposed linear model achieves low error on a range of fabrics and generalizes to different yarns and stitch sizes. This paves the way for applications where having a known yarn distribution is important for accuracy (e.g., simulation) or cost estimation (e.g., design).