publications

2026

1. Priors of U: Dynamic Preference Learning for Embodied Interaction

   Violet Yinuo Han, and Alexandra Ion

   ACM UIST’26, Detroit, MI, USA, Nov 2026

   
   

   Abstract DOI PDF Video

   People have their own habits; from how they organize their workspace, over which mug they reach for in the morning, to how they prepare for cooking. Such preferences are context-dependent, dynamic, and often difficult to articulate. As embodied AI agents, such as robots, enter personal spaces, effective interaction requires understanding these implicit and evolving preferences; otherwise, such systems risk being more hindering than helpful. We propose an architecture that learns user preferences from real-world behavioral observation without requiring explicit instruction or feedback. We introduce a factor-graph-based user model that captures preferences across spatial arrangement, tool selection, and workflow, and continuously adapts as tasks and contexts change. The model expands with new observations, enabling open-ended, in situ learning of user-specific preference patterns. Our evaluation shows accurate, calibrated predictions across domains and robust adaptation over time. We demonstrate personalized robot assistance scenarios illustrating seamless and context-aware human–agent interaction.

   How can an embodied agent learn your habits and preferences just by watching, and keep up as they change?

2. 

   Objects with Arms: How Robot Embodiment Shapes Perception in Physical Collaboration

   Violet Yinuo Han, Ziru Wei, Aiden Yiliu Li, Chris Wu, and Alexandra Ion

   IEEE RO-MAN’26, Kitakyushu, Japan, Aug 2026

   
   

   Abstract PDF Video

   Robots are entering everyday life in increasingly diverse forms, from mobile manipulators and humanoids to robotic furniture, objects, and spaces. We study how such embodiments shape user perception during physical collaboration through an exploratory Wizard-of-Oz study (N = 16), comparing object-embodied manipulation (a toolbox with arms, a kitchen bookstand with arms) with generic standalone arms, across mixed-initiative and human-initiative-only styles. Users characterized object embodiments as unified entities with contextual expertise (10 of 16 participants), whereas generic arms were perceived as separate tools. Object embodiments were rated more likable (p = 0.049), and perceived intelligence increased after collaboration only for object embodiments (p < 0.01). Initiative style did not significantly affect fluency, but participants valued assistance that fell into two functional categories cutting across styles, supporting user decisions through guidance and confirmation, and reducing user workload through anticipatory action, secondary assistance, and independent task contributions. These findings suggest that embedding robotic capabilities into task contexts can change how users conceptualize robot collaborators.

   Does it matter whether a robot arm is attached to a toolbox or stands on its own? How embodiment shapes how people see their robot collaborators.

2025

1. Towards Unobtrusive Physical AI: Augmenting Everyday Objects with Intelligence and Robotic Movement for Proactive Assistance

   Violet Yinuo Han, Jesse T. Gonzalez, Christina Yang, Zhiruo Wang, Scott E. Hudson, and Alexandra Ion

   ACM UIST’25, Busan, Korea, Sep 2025

   
   

   Abstract DOI PDF Video Code

   Users constantly interact with physical, most often passive, objects. Consider if familiar objects instead proactively assisted users, e.g., a stapler moving across the table to help users organize documents, or a knife moving away to prevent injury as the user is inatten- tively about to lean against the countertop. In this paper, we build on the qualities of tangible interaction and focus on recognizing user needs in everyday tasks to enable ubiquitous yet unobtrusive tangible interaction. To achieve this, we introduce an architecture that leverages large language models (LLMs) to perceive users’ environment and activities, perform spatial-temporal reasoning, and generate object actions aligned with inferred user intentions and object properties. We demonstrate the system’s utility provid- ing proactive assistance with multiple objects and in various daily scenarios. To evaluate our system components, we compare our system-generated output for user goal estimation and object action recommendation with human-annotated baselines, with results indicating good agreement.

   What if everyday objects can become proactive robotic agents that offer us help when we need it?

2. Transforming Everyday Objects into Dynamic Interfaces using Smart Flat-Foldable Structures

   Violet Yinuo Han, Amber Yinglei Chen, Mason Zadan, Jesse T. Gonzalez, Dinesh K. Patel, Wendy Fangyu Yu, Carmel Majidi, and Alexandra Ion

   ACM UIST’25, Busan, Korea, Sep 2025

   
   

   Abstract DOI PDF Video

   Dynamic physical interfaces are often dedicated devices designed to adapt their physical properties to user needs. In this paper, we present an actuation system that allows users to transform their existing objects into dynamic physical user interfaces. We design our actuation system to integrate as a self-contained locomotion layer into existing objects that are small-scale, i.e., hand-size rather than furniture-size. We envision that such objects can act as col- laborators: as a studio assistant in a painter’s palette, as tutors in a student’s ruler, or as caretakers for plants evading direct sunlight. The key idea is to decompose the actuation into (1) energy input and (2) steering to achieve a flat form factor. The energy input is provided by simple vibration. We implement steering through differential friction controlled by flat-foldable compliant structures that can be activated electrically. We study the mechanism and its performance, and show its application scenarios enabling dynamic interactions with objects.

   What if there is a "motion layer" that can replace objects’ bottom surfaces, allowing them to move without changing their form and functionalities?

3. A Dynamic Bayesian Network Based Framework for Multimodal Context-Aware Interactions

   Violet Yinuo Han, Tianyi Wang, Hyunsung Cho, Kashyap Todi, Ajoy Savio Fernandes, Andre Levi, Zheng Zhang, Tovi Grossman, Alexandra Ion, and Tanya Jonker

   ACM IUI’25, Cagliari, Italy, Mar 2025

   
   

   Abstract DOI PDF Video

   Multimodal context-aware interactions integrate multiple sensory inputs, such as gaze, gestures, speech, and environmental signals, to provide adaptive support across diverse user contexts. Building such systems is challenging due to the complexity of sensor fusion, real-time decision-making, and managing uncertainties from noisy inputs. To address these challenges, we propose a hybrid approach combining a dynamic Bayesian network (DBN) with a large language model (LLM). The DBN offers a probabilistic framework for modeling variables, relationships, and temporal dependencies, enabling robust, real-time inference of user intent, while the LLM incorporates world knowledge for contextual reasoning. We demonstrate our approach with a tri-level DBN implementation for tangible interactions, integrating gaze and hand actions to infer user intent in real time. A user evaluation with 10 participants in an everyday office scenario showed that our system can accurately and efficiently infer user intentions, achieving 0.83 per frame accuracy, even in complex environments. These results validate the effectiveness of the DBN+LLM framework for multimodal context-aware interactions.

   How can a system efficiently infer user intents from multimodal inputs in real time and generalize to unseen contexts?

2024

1. 

   Robotic Metamaterials: A Modular System for Hands-On Configuration of Ad-Hoc Dynamic Applications

   Zhitong Cui, Shuhong Wang, Violet Yinuo Han, Tucker Rae-Grant, Willa Yunqi Yang, Alan Zhu, Scott E. Hudson, and Alexandra Ion

   ACM CHI’24, Honolulu, HI, May 2024

   
   

   Abstract DOI PDF Video

   We propose augmenting initially passive structures built from simple repeated cells, with novel active units to enable dynamic, shape-changing, and robotic applications. Inspired by metamaterials that can employ mechanisms, we build a framework that allows users to configure cells of this passive structure to allow it to perform complex tasks. A key benefit is that our structures can be repeatedly (re)configured by users inserting our configuration units to turn the passive material into, e.g., locomotion robots, integrated motion platforms, or interactive interfaces, as we demonstrate in this paper.

   What if users can author dynamic applications with modular robotic metamaterials?

2023

1. 

   BlendMR: A Computational Method to Create Ambient Mixed Reality Interfaces

    Best Paper Award

   Violet Yinuo Han, Hyunsung Cho, Kiyosu Maeda, Alexandra Ion, and David Lindlbauer

   ACM ISS’23, Pittsburgh, PA, Nov 2023

   
   

   Abstract DOI PDF Video Talk Code

   Mixed Reality (MR) systems display content freely in space, and present nearly arbitrary amounts of information, enabling ubiquitous access to digital information. This approach, however, introduces clutter and distraction if too much virtual content is shown. We present BlendMR, an optimization-based MR system that blends virtual content onto the physical objects in users’ environments for ambient information display. Our approach takes existing 2D applications and meshes of physical objects as input. It analyses the geometry of the physical objects and identifies regions that are suitable hosts for virtual elements. Using a novel integer programming formulation, our approach then optimally maps selected contents of the 2D applications onto the object, optimizing for factors such as importance and hierarchy of information, viewing angle, and geometric distortion. We evaluate BlendMR by comparing it to a 2D window baseline. Study results show that BlendMR decreases clutter and distraction, and is preferred by users. We demonstrate the applicability of BlendMR in a series of results and usage scenarios.

   What if future always-on MR interfaces can blend into our existing physical objects for ambient information display?

2. 

   Parametric Haptics: Versatile Geometry-Based Tactile Feedback Devices

   Violet Yinuo Han, Abena Boadi-Agyemang, Yuyu Lin, David Lindlbauer, and Alexandra Ion

   ACM UIST’23, San Francisco, CA, Nov 2023

   
   

   Abstract DOI PDF Video Talk

   Haptic feedback is important for immersive, assistive, or multimodal interfaces, but engineering devices that generalize across applications is notoriously difficult. To address the issue of versatility, we propose Parametric Haptics, geometry-based tactile feedback devices that are customizable to render a variety of tactile sensations. To achieve this, we integrate the actuation mechanism with the tactor geometry into passive 3D printable patches, which are then connected to a generic wearable actuation interface consisting of micro gear motors. The key benefit of our approach is that the 3D-printed patches are modular, can consist of varying numbers and shapes of tactors, and that the tactors can be grouped and moved by our actuation geometry over large areas of the skin. The patches are soft, thin, conformable, and easy to customize to different use cases, thus potentially enabling a large design space of diverse tactile sensations. In our user study, we investigate the mapping between geometry parameters of our haptic patches and users’ tactile perceptions. Results indicate a good agreement between our parameters and the reported sensations, showing initial evidence that our haptic patches can produce a wide range of sensations for diverse use scenarios. We demonstrate the utility of our approach with wearable prototypes in immersive Virtual Reality (VR) scenarios, embedded into wearable objects such as glasses, and as wearable navigation and notification interfaces. We support designing such patches with a design tool in Rhino.

   How can haptic experiences be customized, digitalized, shared, and easily integratable into diverse applications and even objects?

3. 

   Permeable Thermistor Temperature Sensors Based on Porous Melamine Foam

   Hugo Souza Oliveira, Niloofar Saeedzadeh Khaanghah, Violet Yinuo Han, Alejandro Carrasco-Pena, Alexandra Ion, Michael Haller, Giuseppe Cantarella, and Niko Münzenrieder

   IEEE Sensors Letters, Nov 2023

   
   

   Abstract DOI

   Flexible sensors and electronics have gained much attention in recent years. They are especially interesting due to their abilities to conform to static and dynamic surfaces while keeping their functionality. These characteristics make them relevant for a wide range of applications, from health care and fitness monitoring to soft robotics. In this work, we go beyond simple mechanical flexibility and present a lightweight and permeable flexible sensor utilizing melamine foam as a substrate. The foam is coated with metallic copper (Cu) and semiconductive Indium-Gallium-Zinc-Oxide (InGaZnO) to form a thermistor-type temperature sensor. The sensor showed a very stable response when cycling the temperature between 25 °C and 51 °C, exhibiting a maximum sensitivity of −01.6%∘C−1 , a permeability of 366.6gm−2h−1 at 24 °C, and a maximum resistance variation of −2.9%RH−1 when varying the relative humidity from 40% to 70%. The device also remained fully functional even after being bent to a radius of 5 mm.

   Lightweight permeable temperature sensors built on melamine foam for flexible wearable applications.