Deciding which idea is worth prototyping is a central concern in iterative design. A prototype should be produced when the expected improvement is high and the cost is low. However, this is hard to decide, because costs can vary drastically: a simple parameter tweak may take seconds, while fabricating hardware consumes material and energy. Such asymmetries, can discourage a designer from exploring the design space. In this paper, we present an extension of cost-aware Bayesian optimization to account for diverse prototyping costs. The method builds on the power of Bayesian optimization and requires only a minimal modification to the acquisition function. The key idea is to use designer-estimated costs to guide sampling toward more cost-effective prototypes. In technical evaluations, the method achieved comparable utility to a cost-agnostic baseline while requiring only \({\approx }70\%\) of the cost; under strict budgets, it outperformed the baseline threefold. A within-subjects study with 12 participants in a realistic joystick design task demonstrated similar benefits. These results show that accounting for prototyping costs can make Bayesian optimization more compatible with real-world design projects.

Explainable AI (XAI) offers solutions to the challenges of predictability and interpretability in adaptive interfaces, particularly in Augmented Reality (AR) systems that dynamically adapt information based on situational contexts. While traditional XAI methods highlight contextual factors influencing adaptations, they often overlook the user’s internal understanding, such as their expertise and contextual perceptions. This omission can result in explanations that feel redundant or obvious. We present XAIUI, a computational approach that generates tailored explanations by integrating the system’s adaptation model with a Bayesian model of the user’s internal representation. Two online studies evaluated XAIUI. In the first study (N = 77), participants ranked XAIUI ’s explanations as most preferred compared to four ablations ( \(\chi^{2}(4)=62.28, {\textrm{p}} < 0.001\) ). In the second study (N = 110), XAIUI ’s explanations were rated significantly less complex ( \(\chi^{2}(4)=840.855, {\textrm{p}} < 0.001\) ) than all ablations, except showing no explanation. Our results demonstrate XAIUI ’s ability to deliver user-centric, concise, and intuitive explanations, highlighting its potential to enhance AI-driven interfaces.

As the number of selectable items increases, point-and-click interfaces rapidly become complex, leading to a decrease in usability. Adaptive user interfaces can reduce this complexity by automatically adjusting an interface to only display the most relevant items. A core challenge for developing adaptive interfaces is to infer user intent and chose adaptations accordingly. Current methods rely on tediously hand-crafted rules or carefully collected user data. Furthermore, heuristics need to be recrafted and data regathered for every new task and interface. To address this issue, we formulate interface adaptation as a multi-agent reinforcement learning problem. Our approach learns adaptation policies without relying on heuristics or real user data, facilitating the development of adaptive interfaces across various tasks with minimal adjustments needed. In our formulation, a user agent mimics a real user and learns to interact with an interface via point-and-click actions. Simultaneously, an interface agent learns interface adaptations, to maximize the user agent's efficiency, by observing the user agent's behavior. For our evaluation, we substituted the simulated user agent with actual users. Our study involved twelve participants and concentrated on automatic toolbar item assignment. The results show that the policies we developed in simulation effectively apply to real users. These users were able to complete tasks with fewer actions and in similar times compared to methods trained with real data. Additionally, we demonstrated our method's efficiency and generalizability across four different interfaces and tasks.

We present Omni, a self-contained 3D haptic feedback system that is capable of sensing and actuating an untethered, passive tool containing only a small embedded permanent magnet. Omni enriches AR, VR and desktop applications by providing an active haptic experience using a simple apparatus centered around an electromagnetic base. The spatial haptic capabilities of Omni are enabled by a novel gradient-based method to reconstruct the 3D position of the permanent magnet in midair using the measurements from eight off-the-shelf hall sensors that are integrated into the base. Omni's 3 DoF spherical electromagnet simultaneously exerts dynamic and precise radial and tangential forces in a volumetric space around the device. Since our system is fully integrated, contains no moving parts and requires no external tracking, it is easy and affordable to fabricate. We describe Omni's hardware implementation, our 3D reconstruction algorithm, and evaluate the tracking and actuation performance in depth. Finally, we demonstrate its capabilities via a set of interactive usage scenarios.

We introduce an optimal control method for electromagnetic haptic guidance systems. Our real-time approach assists users in pen-based tasks such as drawing, sketching or designing. The key to our control method is that it guides users, yet does not take away agency. Existing approaches force the stylus to a continuously advancing setpoint on a target trajectory, leading to undesirable behavior such as loss of haptic guidance or unintended snapping. Our control approach, in contrast, gently pulls users towards the target trajectory, allowing them to always easily override the system to adapt their input spontaneously and draw at their own speed. To achieve this flexible guidance, our optimization iteratively predicts the motion of an input device such as a pen, and adjusts the position and strength of an underlying dynamic electromagnetic actuator accordingly. To enable real-time computation, we additionally introduce a novel and fast approximate model of an electromagnet. We demonstrate the applicability of our approach by implementing it on a prototypical hardware platform based on an electromagnet moving on a bi-axial linear stage, as well as a set of applications. Experimental results show that our approach is more accurate and preferred by users compared to open-loop and time-dependent closed-loop approaches.