Workshop paper (2 pages) presented at the ICSR Workshop on Enriching HRI Research with Qualitative Methods, Virtual, November 2020 (misc)
We will share our experiences designing and conducting structured video-conferencing interviews with autism specialists and utilizing thematic analysis to create qualitative requirements and quantitative specifications for a touch-perceiving robot companion tailored for children with autism. We will also explain how we wrote about our qualitative approaches for a journal setting.
Work-in-progress paper (3 pages) presented at the IEEE Haptics Symposium, Washington, DC, USA, March 2020 (misc)
We present a fabric-based piezoresistive tactile sensor system designed to detect social touch gestures on a robot. The unique sensor design utilizes three layers of low-conductivity fabric sewn together on alternating edges to form an accordion pattern and secured between two outer high-conductivity layers. This five-layer design demonstrates a greater resistance range and better low-force sensitivity than
previous designs that use one layer of low-conductivity fabric with or without a plastic mesh layer. An individual sensor from our system can presently identify six different communication gestures – squeezing, patting, scratching, poking, hand resting without movement, and no touch – with an average accuracy of 90%. A layer of foam can be added beneath the sensor to make a rigid robot more appealing for humans to touch without inhibiting the system’s ability to register social touch gestures.
Paladyn. Journal of Behavioral Robotics, 2020 (article) Accepted
Children with autism need innovative solutions that help them learn to master everyday experiences and cope with stressful situations. We propose that socially assistive robot companions could better understand and react to a child’s needs if they utilized tactile sensing. We examined the existing relevant literature to create an initial set of six tactile-perception requirements, and we then evaluated these requirements through interviews with 11 experienced autism specialists from a variety of backgrounds. Thematic analysis of the comments shared by the specialists revealed three overarching themes: the touch-seeking and touch-avoiding behavior of autistic children, their individual diﬀerences and customization needs, and the roles that a touch-perceiving robot could play in such interactions. Using the interview study feedback, we reﬁned our initial list into seven qualitative requirements that describe robustness and maintainability, sensing range, feel, gesture identiﬁcation, spatial, temporal, and adaptation attributes for the touch-perception system of a robot companion for children with autism. Lastly, by utilizing the literature and current best practices in tactile sensor development and signal processing, we transformed these qualitative requirements into quantitative speciﬁcations. We discuss the implications of these requirements for future HRI research in the sensing, computing, and user research communities.
In Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems (CHI), Glasgow, Scotland, May 2019 (inproceedings)
Creating haptic experiences often entails inventing, modifying, or selecting specialized hardware. However, experience designers are rarely engineers, and 30 years of haptic inventions are buried in a fragmented literature that describes devices mechanically rather than by potential purpose. We conceived of Haptipedia to unlock this trove of examples: Haptipedia presents a device corpus for exploration through metadata that matter to both device and experience designers. It is a taxonomy of device attributes that go beyond physical description to capture potential utility, applied to a growing database of 105 grounded force-feedback devices, and accessed through a public visualization that links utility to morphology. Haptipedia's design was driven by both systematic review of the haptic device literature and rich input from diverse haptic designers. We describe Haptipedia's reception (including hopes it will redefine device reporting standards) and our plans for its sustainability through community participation.
Haptipedia is an online taxonomy, database, and visualization that aims to accelerate ideation of new
haptic devices and interactions in human-computer interaction, virtual reality, haptics, and robotics.
The current version of Haptipedia (105 devices) was created through iterative design, data entry, and
evaluation by our team of experts. Next, we aim to greatly increase the number of devices and keep
Haptipedia updated by soliciting data entry and verification from haptics experts worldwide.
Hands-on demonstration presented at EuroHaptics, Pisa, Italy, June 2018 (misc)
How many haptic devices have been proposed in the last 30 years? How can we leverage this rich source of design knowledge to inspire future innovations? Our goal is to make historical haptic invention accessible through interactive visualization of a comprehensive library – a Haptipedia – of devices that have been annotated with designer-relevant metadata. In this demonstration, participants can explore Haptipedia’s growing library of grounded force feedback devices through several prototype visualizations, interact with 3D simulations of the device mechanisms and movements, and tell us about the attributes and devices that could make Haptipedia a useful resource for the haptic design community.
Work-in-progress paper (3 pages) presented at the IEEE Haptics Symposium, San Francisco, USA, March 2018 (misc)
Much of three decades of haptic device invention is effectively lost to today’s designers: dispersion across time, region, and discipline imposes an incalculable drag on innovation in this field. Our goal is to make historical haptic invention accessible through interactive navigation of a comprehensive library – a Haptipedia – of devices that have been annotated with designer-relevant metadata. To build this open resource, we will systematically mine the literature and engage the haptics community for expert annotation. In a multi-year broad-based initiative, we will empirically derive salient attributes of haptic devices, design an interactive visualization tool where device creators and repurposers can efficiently explore and search Haptipedia, and establish methods and tools to manually and algorithmically collect data from the haptics literature and our community of experts. This paper outlines progress in compiling
an initial corpus of grounded force-feedback devices and their attributes, and it presents a concept sketch of the interface we envision.
Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems