Users hold their mobile phones at varying distances depending on their posture, the application being used, and the task’s nature. Without considering such variation when designing UI target sizes limits the applicability of gaze selection for everyday interaction with mobile devices. Towards this end, we conducted a user study (N = 24) to investigate the implications of different target sizes and viewing across different screen regions. While larger targets generally improve accuracy and decrease precision, accuracy is significantly higher in the horizontal than in the vertical direction. This subsequently led us to find that increasing the tracking area in the vertical direction only, while maintaining the same visual target size, significantly improves accuracy. This suggests that visually smaller targets with larger vertical tracking areas enhance accuracy. Based on our results, we present concrete design guidelines for developers to optimise target sizes on gaze-enabled mobile devices to improve accuracy across varying user-to-screen distances.

We consider the problem of recovering a mental target (e.g., an image of a face) that a participant has in mind from paired EEG (i.e., brain responses) and image (i.e., perceived faces) data collected during interactive sessions without access to labeled information. The problem has been previously explored with labeled data but not via self-calibration, where labeled data is unavailable. Here, we present the first framework and an algorithm, CURSOR, that learns to recover unknown mental targets without access to labeled data or pre-trained decoders. Our experiments on naturalistic images of faces demonstrate that CURSOR can (1) predict image similarity scores that correlate with human perceptual judgments without any label information, (2) use these scores to rank stimuli against an unknown mental target, and (3) generate new stimuli indistinguishable from the unknown mental target (validated via a user study, N=53). We release the brain response data set (N=29), associated face images used as stimuli data, and a codebase to initiate further research on this novel task.

We present the first prototype of Curio, an affordable robot designed for computer science education (CSE). Curio’s goal is to enhance students’ engagement and facilitate CSE. This is achieved by placing students’ smartphones at the center of their experience and letting them designing standalone activities that run on web pages. Such web applications can access all smartphone sensors, including the front and back cameras, and control the robot directly via Web-Bluetooth. This paper provides an overview of Curio’s design philosophy and the technological integration of our first prototype. Videos of Curio in action are available at trycurio.com.

Teleoperating robotic arms can be a challenging task for non-experts, particularly when using complex control devices or interfaces. To address the limitations and challenges of existing teleoperation frameworks, such as cognitive strain, control complexity, robot compatibility, and user evaluation, we propose TELESIM, a modular and plug-and-play framework that enables direct teleoperation of any robotic arm using a digital twin as the interface between users and the robotic system. Due to TELESIM’s modular design, it is possible to control the digital twin using any device that outputs a 3D pose, such as a virtual reality controller or a finger-mapping hardware controller. To evaluate the efficacy and user-friendliness of TELESIM, we conducted a user study with 37 participants. The study involved a simple pick-and-place task, which was performed using two different robots equipped with two different control modalities. Our experimental results show that most users were able to succeed by building at least a tower of 3 cubes in 10 minutes, with only 5 minutes of training beforehand, regardless of the control modality or robot used, demonstrating the usability and user-friendliness of TELESIM.

Gaze is promising for hands-free interaction on mobile devices. However, it is not clear how gaze interaction methods compare to each other in mobile settings. This paper presents the first experiment in a mobile setting that compares three of the most commonly used gaze interaction methods: Dwell time, Pursuits, and Gaze gestures. In our study, 24 participants selected one of 2, 4, 9, 12 and 32 targets via gaze while sitting and while walking. Results show that input using Pursuits is faster than Dwell time and Gaze gestures especially when there are many targets. Users prefer Pursuits when stationary, but prefer Dwell time when walking. While selection using Gaze gestures is more demanding and slower when there are many targets, it is suitable for contexts where accuracy is more important than speed. We conclude with guidelines for the design of gaze interaction on handheld mobile devices.

This demo takes the form of a challenge to the IJCAI community. A physical vault, secured by a 4-digit code, will be placed in the demo area. The author will publicly open the vault by entering the code on a touch-based interface, and as many times as requested. The challenge to the IJCAI participants will be to crack the code, open the vault, and collect its content. The interface is based on previous work on calibration-free interactive systems that enables a user to start instructing a machine without the machine knowing how to interpret the user’s actions beforehand. The intent and the behavior of the human are simultaneously learned by the machine. An online demo and videos are available for readers to participate in the challenge. An additional interface using vocal commands will be revealed on the demo day, demonstrating the scalability of our approach to continuous input signals.

Members of a team are able to coordinate their actions by anticipating the intentions of others. Achieving such implicit coordination between humans and robots requires humans to be able to quickly and robustly predict the robot's intentions, i.e. the robot should demonstrate a behavior that is legible. Whereas previous work has sought to explicitly optimize the legibility of behavior, we investigate legibility as a property that arises automatically from general requirements on the efficiency and robustness of joint human-robot task completion. We do so by optimizing fast and successful completion of joint human-robot tasks through policy improvement with stochastic optimization. Two experiments with human subjects show that robots are able to adapt their behavior so that humans become better at predicting the robot's intentions early on, which leads to faster and more robust overall task completion.

Recent works have explored the use of brain signals to directly control virtual and robotic agents in sequential tasks. So far in such brain-computer interfaces (BCI), an explicit calibration phase was required to build a decoder that translates raw electroencephalography (EEG) signals from the brain of each user into meaningful instructions. This paper proposes a method that removes the calibration phase, and allows a user to control an agent to solve a sequential task. The proposed method assumes a distribution of possible tasks, and infers the interpretation of EEG signals and the task by selecting the hypothesis which best explains the history of interaction. We introduce a measure of uncertainty on the task and on the EEG signal interpretation to act as an exploratory bonus for a planning strategy. This speeds up learning by guiding the system to regions that better disambiguate among task hypotheses. We report experiments where four users use BCI to control an agent on a virtual world to reach a target without any previous calibration process.