Multimodal Attention Tracking in Human-Robot Interaction for Manufacturing Tasks

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Chen Li, AAU
Human-robot interaction is crucial for improving collaboration in industrial settings. This talk presents a multimodal approach that employs a context-aware natural language interface and position tracker to monitor operator attention. By integrating multimodal inputs, including head pose estimation and intent recognition, the system accurately predicts user focus and adjusts robot behaviors accordingly.

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Multimodal Attention Tracking in Human-Robot Interaction for Manufacturing Tasks

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