Rachel Ringe

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 expression in a non-anthropomorphic kitchen assistant robot. Using a within-subjects VR experiment (N=14), participants completed three collaborative cooking tasks while the robot expressed happiness, sadness, and anger via synthesized sounds or colored lights. Results show that effectiveness is emotion-dependent: sound significantly outperformed color for happiness and sadness, while color (red) was more effective for anger. Sound also yielded higher believability, likeability, animacy, and perceived intelligence. Qualitative findings suggest sound’s advantage stems from both its functional benefit in visually demanding tasks and its capacity to create social presence. A key emergent finding is that contextual appropriateness shapes emotion interpretation, indicating that expression design must account for social expectations in collaborative settings.

We present the concept of Intero-Expressive Robots, proposing external representation of internal signals to make hidden states perceivable during interaction. In humans, external indications of interoceptive signals such as respiration or cardiovascular function support attribution of internal states to others (Theory of Mind) by providing information that cannot be inferred from behavior alone. Drawing on interoception as a design analogy, we discuss how exposing robotic variables—such as computational load or energy use—may support analogous forms of state attribution. We position intero-expression as complementary to robot transparency and legibility: externalizing internal dynamics that shape behavior and constrain performance. We describe preliminary feasibility work in a virtual environment, where a robot executes a task while a CPU-derived internal signal is logged. Identical actions produced different loads depending on task context, patterns not predictable from observation alone. This suggests that intero-expressive representations can be interaction-relevant cues.

In the context of AI-based decision support systems, explanations can help users to judge when to trust the AI's suggestion, and when to question it. In this way, human oversight can prevent AI errors and biased decision-making. However, this rests on the assumption that users will consider explanations in enough detail to be able to catch such errors. We conducted an online study on trust in explainable DSS, and were surprised to find that in many cases, participants spent little time on the explanation and did not always consider it in detail. We present an exploratory analysis of this data, investigating what factors impact how carefully study participants consider AI explanations, and how this in turn impacts whether they are open to changing their mind based on what the AI suggests.

This study explores which factors of the visual design of a robot may influence how humans would place it in a collaborative cooking scenario and how these features may influence task delegation. Human participants were placed in a Virtual Reality (VR) environment and asked to set up a kitchen for cooking alongside a robot companion while considering the robot's morphology. We collected multimodal data for the arrangements created by the participants, transcripts of their think-aloud as they were performing the task, and transcripts of their answers to structured post-task questionnaires. Based on analyzing this data, we formulate several hypotheses: humans prefer to collaborate with biomorphic robots; human beliefs about the sensory capabilities of robots are less influenced by the morphology of the robot than beliefs about action capabilities; and humans will implement fewer avoidance strategies when sharing space with gracile robots. We intend to verify these hypotheses in follow-up studies.