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A wide range of studies in Human-Robot Interaction (HRI) has shown that robots can influence the social behavior of humans. This phenomenon is commonly explained by the Media Equation. Fundamental to this theory is the idea that when faced with technology (like robots), people perceive it as a social agent with thoughts and intentions similar to those of humans. This perception guides the interaction with the technology and its predicted impact. However, HRI studies have also reported examples in which the Media Equation has been violated, that is when people treat the influence of robots differently from the influence of humans. To address this gap, we propose a model of Robot Social Influence (RoSI) with two contributing factors. The first factor is a robot’s violation of a person’s expectations, whether the robot exceeds expectations or fails to meet expectations. The second factor is a person’s social belonging with the robot, whether the person belongs to the same group as the robot or a different group. These factors are primary predictors of robots’ social influence and commonly mediate the influence of other factors. We review HRI literature and show how RoSI can explain robots’ social influence in concrete HRI scenarios.
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Empathetic Robot with Transformer-Based Di-alogue Agent
Natural Human-Robot interaction (HRI) attracts considerable interest in letting robots understand the users’ emotional state. This paper demonstrates a method to introduce the affection model to the robotic system’s conversational agent to provide natural and empathetic HRI. We use a large-scale pre-trained language model and fine-tune it on a dialogue dataset with empathetic characteristics. Based on existing studies’ progress, we extend the current method and enable the agent to perform advanced sentiment analysis using the affection model. This dialogue agent will allow the robot to provide natural response along with emotion classification and the estimations of arousal and valence level. We evaluate our model using different metrics, comparing it with the recent studies and showing its emotion detection capacity.
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- Award ID(s):
- 1846658
- PAR ID:
- 10316815
- Date Published:
- Journal Name:
- International Conference on Ubiquitous Robots (UR)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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