Creating 3D assets is essential for game development. Modeling requires time and technical skills, and fast prototyping is challenging, especially for novice designers. To make complex 3D modeling more accessible while strengthening creativity, we present CreatureCreator, a tool that enables users to generate 3D models from an image of a created artifact. We investigated two input modalities: drawing with pen and paper and sculpting with clay. Adding an analog input modality to the generation process could lower the entry barrier for diverse user groups, enabling intuitive and creative modeling. In a within-subject study with game design-experienced participants (n=20), both modalities scored highly for user experience, engagement, and creativity support, with the clay input especially valued for its generated models and enhanced sense of control. The interaction workflow supports playful exploration by allowing users to experiment with parameters and using AI as a production and/or ideation partner.

The way users hold a smartphone depends on the interaction task, yet little is known about the fingers’ engagement with the device’s surfaces beyond the touchscreen. Such an understanding not only opens up opportunities for novel on- and off-screen interactions, but also the device’s possible physical affordances. We present a study (N=23) that examines the hands’ physical engagement with the smartphone beyond the touchscreen across nine mobile interactions. Grasps were annotated from photographs, and contact regions were captured using residual heat traces from grasping the device. Our findings show that fingers and palms adopt a variety of support roles and postures when engaging with the smartphone’s back and side edges. The hand-contact maps reveal distinct patterns, differing in contact frequency and placement. This work contributes an empirical characterisation of hands’ back and edge engagement, highlighting design opportunities for future smartphone usage extending beyond the touchscreen.

Embodiment of users within robotic systems has been explored in human-robot interaction, most often in telepresence and teleoperation. In these applications, synchronized visuomotor feedback can evoke a sense of body ownership and agency, contributing to the experience of embodiment. We extend this work by employing embreathment, the representation of the user's own breath in real time, as a means for enhancing user embodiment experience in robots. In a within-subjects experiment, participants controlled a robotic arm, while its movements were either synchronized or non-synchronized with their own breath. Synchrony was shown to significantly increase body ownership, and was preferred by most participants. We propose the representation of physiological signals as a novel interoceptive pathway for human–robot interaction, and discuss implications for telepresence, prosthetics, collaboration with robots, and shared autonomy.

Animacy is central to HRI, as it is critical for perceptions of intentionality and influences acceptance, interaction quality, and trust. In order to better understand the underlying mechanisms of the sense of animacy and its role in HRI, we conducted an experiment (n=13) using a social robotic object that is ambiguous with respect to its perceived animacy. We examined how brain activity related to a subjective animacy rating of the robot by participants after watching it perform simple gestures. Using functional imaging, we found that the ventral occipitotemporal (VOT) cortex, a region known to distinguish animate from inanimate entities, also responds to robots along this same organizational gradient. In an exploratory analysis, activity in the left VOT was associated with the individual differences in participants’ subjective animacy rating of the robot. This extends established principles of animacy perception to non-humanoid robots and suggests that individual differences in brain responses may shape how robots are experienced. We discuss implications for HRI, including guiding social cue design, informing gesture alignment with robot roles, and raising ethical considerations around attachment and trust.

We present a sensory substitution-based method for representing locations of remote objects in 3D space via haptics. By imitating auditory localization processes, we enable vibrotactile localization abilities similar to those of some spiders, elephants, and other species. We evaluated this concept in virtual reality by modulating the vibration amplitude of two controllers depending on relative locations to a target. We developed two implementations applying this method using either ear or hand locations. A proof-of-concept study assessed localization performance and user experience, achieving under 30° differentiation between horizontal targets with no prior training. This unique approach enables localization by using only two actuators, requires low computational power, and could potentially assist users in gaining spatial awareness in challenging environments. We compare the implementations and discuss the use of hands as ears in motion, a novel technique not previously explored in the sensory substitution literature.

We present the development process, design considerations and generalized system engineering for the ThermalSense sensory substitution system. The system utilizes visual-to-auditory sensory substitution to convey thermal properties through auditory cues. In an online proof of concept study evaluating the system, participants demonstrated a high level of accuracy in recognizing and localizing thermal information which is otherwise invisible in visual scenes. The ability of users to gain proficiency within a short training period was accompanied by a positive user experience evaluation. These results provide an indication of the system’s potential to enable an extended sense, serving as an extension of visual perception to the range of thermal information beyond its natural capacities. The proposed system architecture, together with these findings, set the ground for the further development of this and similar systems, as well as general advances in the study of sensory perception.

This work presents Magnetform, a shape-change display toolkit designed to enable exploration of movement in soft materials. The toolkit allows designers with no technical knowledge to leverage their material expertise to experiment with shape-change. We present the toolkit design, and case studies with two design studios who used the toolkit for 15-days each. Through the presentation of their process, we reflect on two main themes: empowering designers to participate in shape-change exploration; and the developing practice of designing objects which integrate motion. We situate this work as part of the growing efforts in the TEI community to involve designers in the evolution of shape-changing interfaces, and demonstrate how material-oriented designers could contribute to this field in which materiality plays a major role in.

One of the challenges faced by healthy older adults is experiencing feelings of not "being-seen". Companion robots, commonly designed with zoomorphic or humanoid appearance show success among clinical older adults, but healthy older adults find them degrading. We present the design and implementation of a novel non-humanoid robot. The robot's primary function is a cognitive word game. Social interaction is conveyed as a secondary function, using non-verbal gestures, inspired by dancers' movement. In a lab study, 39 healthy older adults interacted with the prototype in 3 conditions: Companion-Function; Game-Function; and No-Function. Results show the non-verbal gestures were associated with feelings of "being-seen", and willingness to accept the robot into their home was influenced by its function, with game significantly higher than companion. We conclude that robot designers should further explore the potential of non-humanoid robots as a new class of companion robots, with a primary function that is not companionship.

Machine Learning services are integrated into various aspects of everyday life. Their underlying processes are typically black-boxed to increase ease-of-use. Consequently, children lack the opportunity to explore such processes and develop essential mental models. We present a gesture recognition research platform, designed to support learning from experience by uncovering Machine Learning building blocks: Data Labeling and Evaluation. Children used the platform to perform physical gestures, iterating between sampling and evaluation. Their understanding was tested in a pre/post experimental design, in three conditions: learning activity uncovering Data Labeling only, Evaluation only, or both. Our findings show that both building blocks are imperative to enhance children's understanding of basic Machine Learning concepts. Children were able to apply their new knowledge to everyday life context, including personally meaningful applications. We conclude that children's interaction with uncovered black boxes of Machine Learning contributes to a better understanding of the world around them.

The biological prototyping revolution is in motion, and new tools are needed to empower HCI researchers, designers, makers, and bio-enthusiasts to experiment with live organisms. We present OpenLH, a liquid handling system that empowers users to conduct accurate and repetitive experiments with live biology in a sterile, open, and affordable way. OpenLH integrates a commercially available robotic arm with custom 3D printed parts, a modified pipette, and a visual block-based programming interface. The system is as accurate as commercial liquid handlers, capable of repetitive tasks in micro-scale accuracy, easy to operate, and supports multi-materials including biomaterials, microorganisms and cell cultures. We describe the system's technical implementation and two custom interfaces. We demonstrate the system's impact for the HCI community with two use cases that include experimentation with live biology in non-traditional fields: visual design using pigment-expressing E.coli, and beer brewing experiment using serial dilution in home context.

Machine Learning (ML) processes are integrated into devices and services that affect many aspects of daily life. As a result, basic understanding of ML concepts becomes essential for people of all ages, including children. We studied if 10--12 years old children can understand basic ML concepts through direct experience with a digital stick-like device, in a WoZ-based experiment. To assess children's understanding we applied an experimental design including a pretest, a gesture recognition training activity, and a posttest. The tests included validating children's understanding of the gesture training activity, other gesture detection processes, and application to ML processes in daily scenarios. Our findings suggest that children are able to understand basic ML concepts, and can even apply them to a new context. We conclude that ML learning activities should allow children to sample their own examples and evaluate them in an iterative way, and proper feedback should be designed to gradually scaffold understanding.

Outdoor play has been proven to be beneficial for children's development. HCI research on Heads-Up Games suggests that the well-known decline in outdoor play can be addressed by adding technology to such activities. However, outdoor play benefits such as social interaction, creative thinking, and physical activity may be compromised when digital features are added. We present the design & implementation of a novel digitally-enhanced outdoor-play prototype. Our evaluation with 48 children revealed that a non-digital version of the novel outdoor play object afforded social play and game invention. Evaluation of the digitally-enhanced version showed reduced collaborative social interaction and reduced creative thinking when compared with baseline. However, we showed that specific sensing and feedback features better supported outdoor play benefits. For example non-accumulated feedback was shown to increase collaborative play and creative thinking in comparison to accumulated feedback. We provide evidence-based recommendations for designers of outdoor play technologies.

Opening encounters are an integral element of everyday social interaction, and are essential for forming and maintaining social relationships between people. We present an abstract non-humanoid robotic object called the Greeting Machine, designed to communicate positive and negative social cues in the context of opening encounters. The design includes a small ball rolling on a larger dome, with a custom gear and lever mechanism that supports a variety of subtle movements. Gestures were designed with movement experts, and were evaluated using a physical first-person qualitative study. Our findings reveal that an abstract robot designed with no specific metaphor can effectively take part in opening encounters. Furthermore, a minimal brief movement, designed as an Approach or Avoid gesture, may be enough to evoke positive and negative experiences. The ability to create opening encounters with low Degree of Freedom abstract robots is promising, both due to the low complexity, low cost, and design flexibility of such devices, and due to the possible generalization of the Approach and Avoid gestures for a variety of morphologies.

We present the initial design process of Scratch Nodes, a sensor-based prototype designed to augment children's social-physical outdoor play. Scratch Nodes has two main components: a hardware device and a tablet-based coding environment. The prototype was designed for 8-12 year old children with the goal of encouraging physical play, social interaction, and "changing the rules" through coding. We extend prior work in the Heads-up Games (HUG) domain by adding a real-time coding environment that directly controls the hardware device, empowering children to change the game's rules in real-time. We argue that the combination of physical play, social interaction, and coding strikes the right balance between the societal need to increase outdoor play & enhance computational thinking skills on one hand and children's need to play, measure, and define their own rules on the other. We present our initial design and implementation process as well as our insights from a preliminary evaluation with six children who tested the prototype.