Human-Robot Interaction Conversational User Enjoyment Scale (HRI CUES)

Understanding user enjoyment is crucial in human-robot interaction (HRI), as it can impact interaction quality and influence user acceptance and long-term engagement with robots, particularly in the context of conversations with social robots. However, current assessment methods rely solely on self-reported questionnaires, failing to capture interaction dynamics. This work introduces the Human-Robot Interaction Conversational User Enjoyment Scale (HRI CUES), a novel scale for assessing user enjoyment from an external perspective during conversations with a robot. Developed through rigorous evaluations and discussions of three annotators with relevant expertise, the scale provides a structured framework for assessing enjoyment in each conversation exchange (turn) alongside overall interaction levels. It aims to complement self-reported enjoyment from users and holds the potential for autonomously identifying user enjoyment in real-time HRI. The scale was validated on 25 older adults' open-domain dialogue with a companion robot that was powered by a large language model for conversations, corresponding to 174 minutes of data, showing moderate to good alignment. Additionally, the study offers insights into understanding the nuances and challenges of assessing user enjoyment in robot interactions, and provides guidelines on applying the scale to other domains.

A Framework for Integrating Gesture Generation Models into Interactive Conversational Agents

Embodied conversational agents (ECAs) benefit from non-verbal behavior for natural and efficient interaction with users. Gesticulation - hand and arm movements accompanying speech - is an essential part of non-verbal behavior. Gesture generation models have been developed for several decades: starting with rule-based and ending with mainly data-driven methods. To date, recent end-to-end gesture generation methods have not been evaluated in a real-time interaction with users. We present a proof-of-concept framework, which is intended to facilitate evaluation of modern gesture generation models in interaction. We demonstrate an extensible open-source framework that contains three components: 1) a 3D interactive agent; 2) a chatbot backend; 3) a gesticulating system. Each component can be replaced, making the proposed framework applicable for investigating the effect of different gesturing models in real-time interactions with different communication modalities, chatbot backends, or different agent appearances. The code and video are available at the project page https://nagyrajmund.github.io/project/gesturebot.