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

Human-Robot Interaction Conversational User Enjoyment Scale (HRI CUES) and this corresponding dataset aim to provide tools for measuring user enjoyment from an external perspective to supplement self-reported user enjoyment responses in human-robot interaction research, with future potential application for autonomous detection of user enjoyment in real-time in robots and agents for adapting conversations contingently to provide enjoyable and long-lasting interactions.

The dataset consists of 25 older adults' (12 men, 13 women) open-domain dialogue with an autonomous companion robot with an integrated large language model (GPT-3.5, text-davinci-003) from participatory design workshops conducted in March 2023. The conversations are annotated for user enjoyment based on HRI CUES by 3 expert annotators, as described in the paper (arXiv:2405.01354). Robot architecture and participatory design workshops are described in DOI: 10.21203/rs.3.rs-2884789/v1.

The conversations are in Swedish. Participants' mean age is 74.6 (SD=5.8). The average interaction duration is 7.4 min (SD=1.5) with 12 to 29 turns. Each turn lasts 5 to 61 seconds (M=17.7, SD=7.2). The total duration of the interactions is 174 min, corresponding to 590 turns.

The dataset contains anonymized transcripts, annotation scores, and self-reported user perceptions. Link: https://zenodo.org/records/12588810

Videos of the interactions are available upon request, contingent upon a signed agreement to maintain data confidentiality in accordance with GDPR regulations.