Computational tools for fabrication often treat materials as passive rather than active participants in design, abstracting away relationships between craftspeople and materials. For craft communities that value relational practices, abstractions limit the adoption and creative uptake of computational tools which might otherwise be beneficial. To understand how better tool design could support richer relations between individuals, tools, and materials, we interviewed expert woodworkers, fiber artists, and metalworkers. We identify three orders of convivial relations central to craft: immediate relations between individuals, tools, and materials; mid-range relations between communities, platforms, and shared materials; and extended relations between institutions, infrastructures, and ecologies. Our analysis shows how craftspeople engage and struggle with convivial relations across all three orders, creating workflows that learn from materials while supporting autonomy. We conclude with design principles for computational tools and infrastructures to better support material dialogue, collective knowledge, and accountability, along with richer and more convivial relations between craftspeople, tools, and the material worlds around them.

When novices fabricate, they naturally start by choosing a workflow (e.g., laser cutting, 3D printing, wire bending) and the corresponding software (e.g., Adobe Illustrator, Fusion 360, Rhino) from a narrow set of options they know. As they advance their design, another workflow might better suit their design intent, but their models remain committed to the original workflow. This prohibits exploration, a learning mechanism fostering informed decision-making.

We investigate what it might look like for a robot to work with a human on a need-finding design task using an affinity diagram. While some recent projects have examined how human–robot teams might explore solutions to design problems, human–robot collaboration in the sensemaking aspects of the design process has not been studied. Designers use affinity diagrams to make sense of unstructured information by clustering paper notes on a work surface. To explore human–robot collaboration on a sensemaking design activity, we developed HIRO, an autonomous robot that constructs affinity diagrams with humans. In a within-user study, 56 participants affinity-diagrammed themes to characterize needs in quotes taken from real-world user data, once alone and once with HIRO. Users spent more time on the task with HIRO than alone, without strong evidence for corresponding effects on cognitive load. In addition, a majority of participants said they preferred to work with HIRO. From post-interaction interviews, we identified eight themes leading to four guidelines for robots that collaborate with humans on sensemaking design tasks: (1) account for the robot’s speed, (2) pursue mutual understanding rather than just correctness, (3) identify opportunities for constructive disagreements, and (4) use other modes of communication in addition to physical materials.

In this paper we explore what role humans might play in designing tools for reinforcement learning (RL) agents to interact with the world. Recent work has explored RL methods that optimize a robot’s morphology while learning to control it, effectively dividing an RL agent’s environment into the external world and the agent’s interface with the world. Taking a user-centered design (UCD) approach, we explore the potential of a human, instead of an algorithm, redesigning the agent’s tool. Using UCD to design for a machine learning agent brings up several research questions, including what it means to understand an RL agent’s experience, beliefs, tendencies, and goals. After discussing these questions, we then present a system we developed to study humans designing a 2D racecar for an RL autonomous driver. We conclude with findings and insights from exploratory pilots with twelve users using this system.

We address the challenge of inferring the design intentions of a human by an intelligent virtual agent that collaborates with the human. First, we propose a dynamic Bayesian network model that relates design intentions, objectives, and solutions during a human's exploration of a problem space. We then train the model on design behaviors generated by a search agent and use the model parameters to infer the design intentions in a test set of real human behaviors. We find that our model is able to infer the exact intentions across three objectives associated with a sequence of design outcomes 31.3% of the time. Inference accuracy is 50.9% for the top two predictions and 67.2% for the top three predictions. For any singular intention over an objective, the model's mean F1-score is 0.719. This provides a reasonable foundation for an intelligent virtual agent to infer design intentions purely from design outcomes toward establishing joint intentions with a human designer. These results also shed light on the potential benefits and pitfalls in using simulated data to train a model for human design intentions.

We describe a physical interactive system for human-robot collaborative design (HRCD) consisting of a tangible user interface (TUI) and a robotic arm that simultaneously manipulates the TUI with the human designer. In an observational study of 12 participants exploring a complex design problem together with the robot, we find that human designers have to negotiate both the physical and the creative space with the machine. They also often ascribe social meaning to the robot's pragmatic behaviors. Based on these findings, we propose four considerations for future HRCD systems: managing the shared workspace, communicating preferences about design goals, respecting different design styles, and taking into account the social meaning of design acts.