A trained humanoid robot can perform human-like crossmodal social attention and conflict resolution
Di Fu, Fares Abawi, Hugo Carneiro, Matthias Kerzel, Ziwei Chen, Erik, Strahl, Xun Liu, Stefan Wermter

TL;DR
This study demonstrates that a trained humanoid robot can emulate human-like crossmodal social attention and resolve conflicts between different sensory inputs, improving social interaction in complex environments.
Contribution
The paper introduces a neurorobotic approach with a trained saliency model enabling a robot to process social cues and resolve crossmodal conflicts, mimicking human attention responses.
Findings
Humans perform better in congruent audio-visual conditions.
The robot with the trained model replicates human-like attention responses.
Crossmodal conflict resolution enhances social attention in robots.
Abstract
To enhance human-robot social interaction, it is essential for robots to process multiple social cues in a complex real-world environment. However, incongruency of input information across modalities is inevitable and could be challenging for robots to process. To tackle this challenge, our study adopted the neurorobotic paradigm of crossmodal conflict resolution to make a robot express human-like social attention. A behavioural experiment was conducted on 37 participants for the human study. We designed a round-table meeting scenario with three animated avatars to improve ecological validity. Each avatar wore a medical mask to obscure the facial cues of the nose, mouth, and jaw. The central avatar shifted its eye gaze while the peripheral avatars generated sound. Gaze direction and sound locations were either spatially congruent or incongruent. We observed that the central avatar's…
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Taxonomy
TopicsSocial Robot Interaction and HRI · Face Recognition and Perception · Visual Attention and Saliency Detection
