arXiv subjects

Rachel Schuchert

Publications and source records attributed to Rachel Schuchert.

3 recordsLinked to original sources

arXiv2026

Aura: Dynamic Intra-Turn Emotion-Aware Adaptation of Large Language Model Responses

Effective human-AI interaction requires systems that dynamically adapt to a user's behavior and evolving understanding. When users interact with Large Language Models (LLMs), these models typically respond to prompts without sensing the user's immediate reactions. This lack of communicative synchrony can lead to information overload or leave confusion unresolved in real time. In this paper, we introduce Aura, a framework that enables LLM systems to dynamically modulate output based on a user's evolving emotions. Aura's Perception Module continuously estimates the user's emotional state from facial expressions. Our Policy Module then selects interventions through a probabilistic belief model. Finally, Aura's Generation Module uses parameter-efficient Low-Rank Adaptation (LoRA) adapters to produce contextually tailored responses mid-turn during response generation. We evaluated Aura in a within-subjects user study (N=20) on information-seeking tasks, where it achieved statistically significantly higher normalized perceived learning gains than a Llama-3 baseline and reduced interaction time by 21% relative to existing LLM baselines (GPT-4o, Llama-3). Our results indicate that real-time, context-sensitive interventions can improve learning efficiency and user satisfaction without observable degradation in factual accuracy. Aura thus supports the potential for more responsive and effective human-AI interaction.

cs.HC↗

arXiv2026

EgoPHI: Estimating Contact and Force from Egocentric Vision

Understanding hand-object interaction from egocentric vision is essential for modeling how people physically engage with the surrounding world. Yet reasoning about physically grounded interaction requires estimating the forces acting on hands and objects, beyond localizing contact. We present EgoPHI, the first method that jointly estimates dense contact maps and 3D force distributions on hand and object meshes from a single monocular RGB image and object geometry. To address the lack of scalable ground-truth force annotations, we introduce a physics-based simulation pipeline that augments existing hand-object datasets with dense per-vertex force supervision. EgoPHI then learns dense 3D contact and force on interacting hand and articulated object meshes, extending vision-based force estimation beyond image-space or planar settings. Our evaluation on in-distribution and out-of-distribution benchmarks shows that EgoPHI improves force estimation over existing approaches while generalizing to unseen datasets. To evaluate sim-to-real transfer, we constructed two physical objects that capture dense object contact and force magnitude and used them to record a dataset of interactions from eight participants across diverse touch and grasp types. Our results demonstrate that EgoPHI recovers meaningful 3D contact and force distributions in simulated, out-of-distribution, and real-world settings, advancing egocentric hand-object understanding from contact localization toward physically grounded interaction reasoning.

cs.CV↗

arXiv2026

Retrofitting Existing 3D Objects with Surface-Conforming Capacitive Sensing

Augmenting the surface of 3D objects with capacitive sensing is challenging when their volumes cannot be modified. In this paper, we present a generative computational fabrication pipeline that retrofits surface-only sensor layouts to 3D geometries for multi-touch interaction. Our method scans a real-world object to obtain its 3D mesh, generates and optimizes a 3D sensor design of drive and sense lines for mutual-capacitance sensing under physical and hardware constraints, and unfolds the design into individual 2D stencils that can be cut from conductive material. Our fabrication pipeline cuts these stencils from thin copper foil with a vinyl cutter and then assists manual sensor attachment by projecting the sensor design onto the dynamically registered real-world object. We connect the resulting electrode mesh to a mutual-capacitance scanning controller and resolve touch interaction in real time. We demonstrate our approach with four 3D geometries and evaluate our method and fabrication pipeline on them.

cs.HC↗