The maker movement embodies a resurgence in DIY creation, merging physical craftsmanship and arts with digital technology support. However, mere technological skills and creativity are insufficient for economically and psychologically sustainable practice. By illuminating and smoothing the path from "maker" to "maker entrepreneur," we can help broaden the viability of making as a livelihood. Our research centers on makers who design, produce, and sell physical goods. In this work, we explore the transition to entrepreneurship for these makers and how technology can facilitate this transition online and offline. We present results from interviews with 20 USA-based maker entrepreneurs (i.e., lamps, stickers), six creative service entrepreneurs (i.e., photographers, fabrication), and seven support personnel (i.e., art curator, incubator director). Our findings reveal that many maker entrepreneurs 1) are makers first and entrepreneurs second; 2) struggle with business logistics and learn business skills as they go; and 3) are motivated by non-monetary values. We discuss training and technology-based design implications and opportunities for addressing challenges in developing economically sustainable businesses around making.

Humans have the ability to anticipate what will happen in their environment based on perceived information. Their anticipation is often manifested as an externally observable behavioral reaction, which cues other people in the environment that something bad might happen. As robots become more prevalent in human spaces, robots can leverage these visible anticipatory responses to assess whether their own actions might be "a bad idea?" In this study, we delved into the potential of human anticipatory reaction recognition to predict outcomes. We conducted a user study wherein 30 participants watched videos of action scenarios and were asked about their anticipated outcome of the situation shown in each video ("good" or "bad"). We collected video and audio data of the participants reactions as they were watching these videos. We then carefully analyzed the participants’ behavioral anticipatory responses; this data was used to train machine learning models to predict anticipated outcomes based on human observable behavior. Reactions are multimodal, compound and diverse, and we find significant differences in facial reactions. Model performances are around 0.5-0.6 test accuracy, and increase notably when nonreactive participants are excluded from the dataset. We discuss the implications of these findings and future work. This research offers insights into improving the safety and efficiency of human-robot interactions, contributing to the evolving field of robotics and human-robot collaboration.

For a robot to repair its own error, it must first know it has made a mistake. One way that people detect errors is from the implicit reactions from bystanders - their confusion, smirks, or giggles clue us in that something unexpected occurred. To enable robots to detect and act on bystander responses to task failures, we developed a novel method to elicit bystander responses to human and robot errors. Using 46 different stimulus videos featuring a variety of human and machine task failures, we collected a total of 2,452 webcam videos of human reactions from 54 participants. To test the viability of the collected data, we used the bystander reaction dataset as input to a deep-learning model, BADNet, to predict failure occurrence. We tested different data labeling methods and learned how they affect model performance, achieving precisions above 90%. We discuss strategies (manual labelling, failure-vs-control, and failure-time) used to model bystander reactions and predict failure, and how this approach can be used in real-world robotic deployments to detect errors and improve robot performance. As part of this work, we also contribute with the “Bystander Affect Detection” (BAD) dataset of bystander reactions, supporting the development of better prediction models.

Sketching is a widely used activity in creative processes. One can argue that we now have Artificial Intelligence (AI) capable of making better drawings and sketches than humans. Some computational drawing applications build upon human input, be it text, an input image, or even a hand-drawn path using the cursor. However, drawing along a present-day computer yields a very different experience from drawing collaboratively with another person. For one, the shared canvas is likely not a physical sheet of paper but rather a screen of some sort. Even with paper-like computer interfaces, the computer mainly draws back virtually, needing no material embodiment to make a drawing. My research investigates human collaboration with computers that draw, specifically focused on the act of sketching. I build exploratory physical systems that allow people to draw and sketch together with a computer. Through my studies, I investigate interaction design to allow for human-computer collaborative sketching. My initial studies with a first experimental platform show that the computers are social actors (CASA) paradigm also applies to non-anthropomorphic pen plotters when performing a Tic-Tac-Toe playing task.

High-fidelity driving simulators can act as testbeds for designing in-vehicle interfaces or validating the safety of novel driver assistance features. In this system paper, we develop and validate the safety of a mixed reality driving simulator system that enables us to superimpose virtual objects and events into the view of participants engaging in real-world driving in unmodified vehicles. To this end, we have validated the mixed reality system for basic driver cockpit and low-speed driving tasks, comparing the use of the system with non-headset and with the headset driving conditions, to ensure that participants behave and perform similarly using this system as they would otherwise. This paper outlines the operational procedures and protocols for using such systems for cockpit tasks (like using the parking brake, reading the instrument panel, and turn signaling) as well as basic low-speed driving exercises (such as steering around corners, weaving around obstacles, and stopping at a fixed line) in ways that are safe, effective, and lead to accurate, repeatable data collection about behavioral responses in real-world driving tasks.

We developed a system to explore expressiveness for a robot playing Tic-Tac-Toe against a human. Our robot is based around a pen plotter which performs expressions through the modalities of motion and drawing, aiming to enhance the social engagement of the human-robot interaction.

We present an exploratory human participant study (N=3) examining how people interact with a pen-plotting robot that interactively plays Tic-Tac-Toe on a shared physical sheet of paper. Each participant played a round of Tic-Tac-Toe against the robot, while we observed. We particularly focused our observations on the participants’ physical and social behaviors during game interaction, as well as in-moment reactions from the participants. Following each game, we performed semi-structured qualitative interviews to understand the user’s experience interacting with the robot. Our questions were designed to elicit comparisons of their experience with less tangible interactions that players might have with a computer or phone-based app, as well as more traditional interactions that players might have with other people. We found that participants directly addressed the robot by talking to it during play and openly expressed competitiveness against the robot. Furthermore, participants displayed careful movements around the robot and attentively observed its behaviors. Based on these initial insights from our exploratory study, we are planning future experiments to investigate the effect that the mutuality of the physical Tic-Tac-Toe interaction has on social responses to the robot to understand what this implies for embodied and tangible interaction design.