We present AutoOptimization, a novel multi-objective optimization framework for adapting user interfaces. From a user’s verbal preferences for changing a UI, our framework guides a prioritization-based Pareto frontier search over candidate layouts. It selects suitable objective functions for UI placement while simultaneously parameterizing them according to the user’s instructions to define the optimization problem. A solver then generates a series of optimal UI layouts, which our framework validates against the user’s instructions to adapt the UI with the final solution. Our approach thus overcomes the previous need for manual inspection of layouts and the use of population averages for objective parameters. We integrate multiple agents sequentially within our framework, enabling the system to leverage their reasoning capabilities to interpret user preferences, configure the optimization problem, and validate optimization outcomes. We evaluate each step of our framework inside a Mixed Reality use case and demonstrate that AutoOptimization effectively increases the usability of UI adaptation schemes.

Generative models are increasingly powerful, yet users struggle to guide them through prompts. The generative process is difficult to control and unpredictable, and user instructions may be ambiguous or under-specified. Prior prompt refinement tools heavily rely on human effort, while prompt optimization methods focus on numerical functions and are not designed for human-centered generative tasks, where feedback is better expressed as binary preferences and demands convergence within few iterations. We present APPO, a preference-guided prompt optimization algorithm. Instead of iterating prompts, users only provide binary preferential feedback. APPO adaptively balances its strategies between exploiting user feedback and exploring new directions, yielding effective and efficient optimization. We evaluate APPO on image generation, and the results show APPO enables achieving satisfactory outcomes in fewer iterations with lower cognitive load than manual prompt editing. We anticipate APPO will advance human-AI collaboration in generative tasks by leveraging user preferences to guide complex content creation.

Human-in-the-loop optimization identifies optimal interface designs by iteratively observing user performance. However, it often requires numerous iterations due to the lack of prior information. While recent approaches have accelerated this process by leveraging previous optimization data, collecting user data remains costly and often impractical. We present a conceptual framework, Human-in-the-Loop Optimization with Model-Informed Priors (HOMI), which augments human-in-the-loop optimization with a training phase where the optimizer learns adaptation strategies from diverse, synthetic user data generated with predictive models before deployment. To realize HOMI, we introduce Neural Acquisition Function+ (NAF+), a Bayesian optimization method featuring a neural acquisition function trained with reinforcement learning. NAF+ learns optimization strategies from large-scale synthetic data, improving efficiency in real-time optimization with users. We evaluate HOMI and NAF+ with mid-air keyboard optimization, a representative VR input task. Our work presents a new approach for more efficient interface adaptation by bridging in situ and in silico optimization processes.

Optimal input settings vary across users due to differences in motor abilities and personal preferences, which are typically addressed by manual tuning or calibration. Although human-in-the-loop optimization has the potential to identify optimal settings during use, it is rarely applied due to its long optimization process. A more efficient approach would continually leverage data from previous users to accelerate optimization, exploiting shared traits while adapting to individual characteristics. We introduce the concept of Continual Human-in-the-Loop Optimization and a Bayesian optimization-based method that leverages a Bayesian-neural-network surrogate model to capture population-level characteristics while adapting to new users. We propose a generative replay strategy to mitigate catastrophic forgetting. We demonstrate our method by optimizing virtual reality keyboard parameters for text entry using direct touch, showing reduced adaptation times with a growing user base. Our method opens the door for next-generation personalized input systems that improve with accumulated experience.

Supernumerary robotic limbs (SRLs) are robotic structures integrated closely with the user's body, which augment human physical capabilities and necessitate seamless, naturalistic human-machine interaction. For effective assistance in physical tasks, enabling SRLs to hand over objects to humans is crucial. Yet, designing heuristic-based policies for robots is time-consuming, difficult to generalize across tasks, and results in less human-like motion. When trained with proper datasets, generative models are powerful alternatives for creating naturalistic handover motions. We introduce 3HANDS, a novel dataset of object handover interactions between a participant performing a daily activity and another participant enacting a hip-mounted SRL in a naturalistic manner. 3HANDS captures the unique characteristics of SRL interactions: operating in intimate personal space with asymmetric object origins, implicit motion synchronization, and the user's engagement in a primary task during the handover. To demonstrate the effectiveness of our dataset, we present three models: one that generates naturalistic handover trajectories, another that determines the appropriate handover endpoints, and a third that predicts the moment to initiate a handover. In a user study (N=10), we compare the handover interaction performed with our method compared to a baseline. The findings show that our method was perceived as significantly more natural, less physically demanding, and more comfortable.

Affordances, a foundational concept in human-computer interaction and design, have traditionally been explained by direct-perception theories, which assume that individuals perceive action possibilities directly from the environment. However, these theories fall short of explaining how affordances are perceived, learned, refined, or misperceived, and how users choose between multiple affordances in dynamic contexts. This paper introduces a novel affordance theory grounded in Computational Rationality, positing that humans construct internal representations of the world based on bounded sensory inputs. Within these internal models, affordances are inferred through two core mechanisms: feature recognition and hypothetical motion trajectories. Our theory redefines affordance perception as a decision-making process, driven by two components: confidence (the perceived likelihood of successfully executing an action) and predicted utility (the expected value of the outcome). By balancing these factors, individuals make informed decisions about which actions to take. Our theory frames affordances perception as dynamic, continuously learned, and refined through reinforcement and feedback. We validate the theory via thought experiments and demonstrate its applicability across diverse types of affordances (e.g., physical, digital, social). Beyond clarifying and generalizing the understanding of affordances across contexts, our theory serves as a foundation for improving design communication and guiding the development of more adaptive and intuitive systems that evolve with user capabilities.

Adjusting visual parameters such as brightness and contrast is common in our everyday experiences. Finding the optimal parameter setting is challenging due to the large search space and the lack of an explicit objective function, leaving users to rely solely on their implicit preferences. Prior work has explored Preferential Bayesian Optimization (PBO) to address this challenge, involving users to iteratively select preferred designs from candidate sets. However, PBO often requires many rounds of preference comparisons, making it more suitable for designers than everyday end-users. We propose Meta-PO, a novel method that integrates PBO with meta-learning to improve sample efficiency. Specifically, Meta-PO infers prior users' preferences and stores them as models, which are leveraged to intelligently suggest design candidates for the new users, enabling faster convergence and more personalized results. An experimental evaluation of our method for appearance design tasks on 2D and 3D content showed that participants achieved satisfactory appearance in 5.86 iterations using Meta-PO when participants shared similar goals with a population (e.g., tuning for a "warm"look) and in 8 iterations even generalizes across divergent goals (e.g., from "vintage", "warm", to "holiday"). Meta-PO makes personalized visual optimization more applicable to end-users through a generalizable, more efficient optimization conditioned on preferences, with the potential to scale interface personalization more broadly.

3D Mixed Reality interfaces have nearly unlimited space for layout placement, making automatic UI adaptation crucial for enhancing the user experience. Such adaptation is often formulated as a multi-objective optimization (MOO) problem, where multiple, potentially conflicting design objectives must be balanced. However, selecting a final layout is challenging since MOO typically yields a set of trade-offs along a Pareto frontier. Prior approaches often required users to manually explore and evaluate these trade-offs, a time-consuming process that disrupts the fluidity of interaction. To eliminate this manual and laborous step, we propose a novel optimization approach that efficiently determines user preferences from a minimal number of UI element adjustments. These determined rankings are translated into priority levels, which then drive our priority-based MOO algorithm. By focusing the search on user-preferred solutions, our method not only identifies UIs that are more aligned with user preferences, but also automatically selects the final design from the Pareto frontier; ultimately, it minimizes user effort while ensuring personalized layouts. Our user study in a Mixed Reality setting demonstrates that our preference-guided approach significantly reduces manual adjustments compared to traditional methods, including fully manual design and exhaustive Pareto front searches, while maintaining high user satisfaction. We believe this work opens the door for more efficient MOO by seamlessly incorporating user preferences.

Wrist-based input often requires tuning parameter settings in correspondence to between-user and between-session differences, such as variations in hand anatomy, wearing position, posture, etc. Traditionally, users either work with predefined parameter values not optimized for individuals or undergo time-consuming calibration processes. We propose an online Bayesian Optimization (BO)-based method for rapidly determining the user-specific optimal settings of wrist-based pointing. Specifically, we develop a meta-Bayesian optimization (meta-BO) method, differing from traditional human-in-the-loop BO: By incorporating meta-learning of prior optimization data from a user population with BO, meta-BO enables rapid calibration of parameters for new users with a handful of trials. We evaluate our method with two representative and distinct wrist-based interactions: absolute and relative pointing. On a weighted-sum metric that consists of completion time, aiming error, and trajectory quality, meta-BO improves absolute pointing performance by 22.92% and 21.35% compared to BO and manual calibration, and improves relative pointing performance by 25.43% and 13.60%.

Selecting a target in a 3D environment is often challenging, especially with small/distant targets or when sensor noise is high. To facilitate selection, target-inference methods must be accurate, fast, and account for noise and motor variability. However, traditional data-free approaches fall short in accuracy since they ignore variability. While data-driven solutions achieve higher accuracy, they rely on extensive human datasets so prove costly, time-consuming, and transfer poorly. In this paper, we propose a novel approach that leverages biomechanical simulation to produce synthetic motion data, capturing a variety of movement-related factors, such as limb configurations and motor noise. Then, an inference model is trained with only the simulated data. Our simulation-based approach improves transfer and lowers cost; variety-rich data can be produced in large quantities for different scenarios. We empirically demonstrate that our method matches the accuracy of human-data-driven approaches using data from seven users. When deployed, the method accurately infers intended targets in challenging 3D pointing conditions within 5–10 milliseconds, reducing users’ target-selection error by 71% and completion time by 35%.

Computational methods can potentially facilitate user interface design by complementing designer intuition, prior experience, and personal preference. Framing a user interface design task as a multi-objective optimization problem can help with operationalizing and structuring this process at the expense of designer agency and experience. While offering a systematic means of exploring the design space, the optimization process cannot typically leverage the designer’s expertise in quickly identifying that a given “bad” design is not worth evaluating. We here examine a cooperative approach where both the designer and optimization process share a common goal and work in partnership by establishing a shared understanding of the design space. We tackle the research question: How can we foster cooperation between the designer and a systematic optimization process in order to best leverage their combined strength? We introduce and present an evaluation of a cooperative approach that allows the user to express their design insight and work in concert with a multi-objective design process. We find that the cooperative approach successfully encourages designers to explore more widely in the design space than when they are working without assistance from an optimization process. The cooperative approach also delivers design outcomes that are comparable to an optimization process run without any direct designer input but achieves this with greater efficiency and substantially higher designer engagement levels.

Affordance refers to the perception of possible actions allowed by an object. Despite its relevance to human–computer interaction, no existing theory explains the mechanisms that underpin affordance-formation; that is, how affordances are discovered and adapted via interaction. We propose an integrative theory of affordance-formation based on the theory of reinforcement learning in cognitive sciences. The key assumption is that users learn to associate promising motor actions to percepts via experience when reinforcement signals (success/failure) are present. They also learn to categorize actions (e.g., “rotating” a dial), giving them the ability to name and reason about affordance. Upon encountering novel widgets, their ability to generalize these actions determines their ability to perceive affordances. We implement this theory in a virtual robot model, which demonstrates human-like adaptation of affordance in interactive widgets tasks. While its predictions align with trends in human data, humans are able to adapt affordances faster, suggesting the existence of additional mechanisms.

Designers reportedly struggle with design optimization tasks where they are asked to find a combination of design parameters that maximizes a given set of objectives. In HCI, design optimization problems are often exceedingly complex, involving multiple objectives and expensive empirical evaluations. Model-based computational design algorithms assist designers by generating design examples during design, however they assume a model of the interaction domain. Black box methods for assistance, on the other hand, can work with any design problem. However, virtually all empirical studies of this human-in-the-loop approach have been carried out by either researchers or end-users. The question stands out if such methods can help designers in realistic tasks. In this paper, we study Bayesian optimization as an algorithmic method to guide the design optimization process. It operates by proposing to a designer which design candidate to try next, given previous observations. We report observations from a comparative study with 40 novice designers who were tasked to optimize a complex 3D touch interaction technique. The optimizer helped designers explore larger proportions of the design space and arrive at a better solution, however they reported lower agency and expressiveness. Designers guided by an optimizer reported lower mental effort but also felt less creative and less in charge of the progress. We conclude that human-in-the-loop optimization can support novice designers in cases where agency is not critical.

Designing a push-button with desired sensation and performance is challenging because the mechanical construction must have the right response characteristics. Physical simulation of a button's force-displacement (FD) response has been studied to facilitate prototyping; however, the simulations' scope and realism have been limited. In this paper, we extend FD modeling to include vibration (V) and velocity-dependence characteristics (V). The resulting FDVV models better capture tactility characteristics of buttons, including snap. They increase the range of simulated buttons and the perceived realism relative to FD models. The paper also demonstrates methods for obtaining these models, editing them, and simulating accordingly. This end-to-end approach enables the analysis, prototyping, and optimization of buttons, and supports exploring designs that would be hard to implement mechanically.

We present Dwell+, a method that boosts the effectiveness of typical dwell selection by augmenting the passive dwell duration with active haptic ticks which promptly drives rapid switches of modes forward through the user's skin sensations. In this way, Dwell+ enables multi-level dwell selection using rapid haptic ticks. To select a mode from a button, users dwell-touch the button until the mode of selection is haptically prompted. Our haptic stimulation design consists of a short 10ms vibrotacile feedback that indicates a mode arriving and a break that separates consecutive modes. We first tested the effectiveness of 170ms, 150ms, 130ms, and 110ms intervals between modes for a 10-level selection. The results reveal that 3-beats-per-chunk rhythm design, e.g., displaying longer 25ms vibrations initially for all three modes, could potentially achieve higher accuracy. The second study reveals significant improvement wherein a 94.5% accuracy was achieved for a 10-level Dwell+ selection using the 170ms interval with 3-beats-per-chunk design, and a 93.82% rate of accuracy using the more frequent 150ms interval with similar chunks for 5-level selection. The performance of conducting touch and receiving vibration from disparate hands was investigated for our final study to provide a wider range of usage. Our applications demonstrated implementing Dwell+ across interfaces, such as text input on a smartwatch, enhancing touch space for HMDs, boosting modalities of stylus-based tool selection, and extending the input vocabulary of physical interfaces.

360-degree video contains a full field of environmental content. However, browsing these videos, either on screens or through head-mounted displays (HMDs), users consume only a subset of the full field of view per a natural viewing experience. This causes a search problem when a region-of-interest (ROI) in a video is outside of the current field of view (FOV) on the screen, or users may search for non-existing ROIs. We propose Outside-In, a visualization technique which re-introduces off-screen regions-of-interest (ROIs) into the main screen as spatial picture-in-picture (PIP) previews. The geometry of the preview windows further encodes a ROI's relative location vis-à-vis the main screen view, allowing for effective navigation. In an 18-participant study, we compare Outside-In with traditional arrow-based guidance within three types of 360-degree video. Results show that Outside-In outperforms in regard to understanding spatial relationship, the storyline of the content and overall preference. Two applications are demonstrated for use with Outside-In in 360-degree video navigation with touchscreens, and live telepresence.

This paper presents EdgeVib, a system of spatiotemporal vibration patterns for delivering alphanumeric characters on wrist-worn vibrotactile displays. We first investigated spatiotemporal pattern delivery through a watch-back tactile display by performing a series of user studies. The results reveal that employing a 2×2 vibrotactile array is more effective than employing a 3×3 one, because the lower-resolution array creates clearer tactile sensations in less time consumption. We then deployed EdgeWrite patterns on a 2×2 vibrotactile array to determine any difficulties of delivering alphanumerical characters, and then modified the unistroke patterns into multistroke EdgeVib ones on the basis of the findings. The results of a 24-participant user study reveal that the recognition rates of the modified multistroke patterns were significantly higher than the original unistroke ones in both alphabet (85.9% vs. 70.7%) and digits (88.6% vs. 78.5%) delivery, and a further study indicated that the techniques can be generalized to deliver two-character compound messages with recognition rates higher than 83.3%. The guidelines derived from our study can be used for designing watch-back tactile displays for alphanumeric character output.