Rainer Malaka

Extended Reality for Enhancing Surgery: Research Directions to Transform the Operating Environment

Emotional Beeps and Blinks: Exploring Emotional Expressiveness in Non-Anthropomorphic Robots Through Sound Design and Color

Effective affective communication is essential for collaborative human-robot interaction, as emotional expressions can enhance trust and collaboration with robots. This paper addresses this need by investigating whether a musically-informed sound design or a color-based display is more effective for emotional expressio...

Emotional Beeps and Blinks: Exploring Emotional Expressiveness in Non-Anthropomorphic Robots Through Sound Design and Color

Investigation of whether a musically-informed sound design or a color-based display is more effective for emotional expression in a non-anthropomorphic kitchen assistant robot shows that effectiveness is emotion-dependent.

Tactile Search: Enhancing Targeting in 3D Space

Visual search is crucial in daily life, from scanning for relevant information to spotting signs of danger. When sensory channels are overloaded or degraded, cognitive tasks can be supported by crossmodal information representations through vibrotactile cues. We introduce Tactile Search, an approach that uses modulatio...

CreatureCreator: Co-Creating 3D Models with AI for Video Games via Sculpting and Drawing

Creating 3D assets is essential for game development. Modeling requires time and technical skills, and fast prototyping is challenging, especially for novice designers. To make complex 3D modeling more accessible while strengthening creativity, we present CreatureCreator, a tool that enables users to generate 3D models...

ADAPTIC: Adapting Dialog and Pragmatic Traits in Context

This workshop proposes a paradigm shift toward designing context-specific CUIs that actively support communicative success through pragmatic cues, and brings together perspectives from HCI and social sciences to explore how users appropriate conversational AI across domains.

Linguistic Uncertainty Markers for Trust Calibration in AI-Assisted Decision-Making

While participants rated hedged and unhedged AI as equally trustworthy and likely to be correct, they were significantly less likely to follow hedged advice in a binary choice, and how linguistic markers can be used to calibrate user reliance to model certainty is discussed.