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Modeling interpersonal perception in dyadic interactions: towards robot-assisted social mediation in the real world
Hifza Javed1, Weinan Wang1,2, Affan Bin Usman1
1Honda Research Institute USA, Inc., San Jose, CA, United States.
Frontiers in Robotics and AI
|December 13, 2024
Summary
Social robots can improve interactions by understanding relational affect. This new multimodal model predicts relational affect from audiovisual cues, enabling robots to mediate conversations effectively.
Area of Science:
- Robotics
- Artificial Intelligence
- Human-Computer Interaction
Background:
- Social mediator robots aim to enhance human interactions, but require understanding complex interpersonal dynamics.
- Current affect recognition models often focus on individual emotions, neglecting crucial interpersonal relational affect.
Purpose of the Study:
- To develop a multimodal, multi-perspective computational model for recognizing relational affect in human interactions.
- To enable social robots to better understand and mediate social dynamics in real-time.
Main Methods:
- Utilized a multimodal dataset of conversations from uncontrolled settings.
- Extracted audiovisual features to capture affective behaviors indicative of relational affect.
- Developed a model considering interactants' perspectives to predict relational affect.
Main Results:
- The proposed model demonstrates early capabilities in predicting relational affect.
- The model can potentially enable proactive interventions in negative interactions and enhance neutral/positive dialogues.
- The model shows advantages for real-world applicability in social mediation.
Conclusions:
- Computational models of relational affect are crucial for effective social mediation by robots.
- This work advances the development of AI systems that can understand and navigate complex human social dynamics.
- Further research is needed to address the complexities and subjectivity of human interactions for real-world deployment.
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