Alessio Galatolo, Iolanda Leite and Katie Winkle

2023 32nd IEEE International Conference on Robot & Human Interactive Communication (RO-MAN).


Previous works in Human-Robot Interaction have demonstrated the positive potential benefit of designing social robots which express specific personalities. In this work, we focus specifically on the adaptation of language (as the choice of words, their order, etc.) following the extraversion trait. We look to investigate whether current language models could support more autonomous generations of such personality-expressive robot output. We examine the performance of two models with user studies evaluating (i) raw text output and (ii) text output when used within multi-modal speech from the Furhat robot. We find that the ability to successfully manipulate perceived extraversion sometimes varies across different dialogue topics. We were able to achieve correct manipulation of robot personality via our language adaptation, but our results suggest further work is necessary to improve the automation and generalisation abilities of these models.

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Extraversion rating in the text-only study for each model x personality combination across each of our three different dialogues, shown on a scale from 0 to 6. Better as the Ext score gets higher than the Int score. Error bars are given by confidence interval. Results of the models in the text-only study across all the dialogues in fluency on a scale from 0 to 6. Higher is better. A screenshot of the video shown in the robot study Plots with the ascribed extraversion for the video-based study on a scale from 0 to 6. Plots with the ascribed fluency for the video-based study on a scale from 0 to 6. Plots with the ascribed RoSAS warmth for the video-based study on a scale from 0 to 4. Plots with the ascribed extraversion comparing text-only and video-based study. Plots with the ascribed fluency comparing text-only and video-based study.
The images shown in the paper.