Linlin Cheng — accepted AI-conference papers

2026

Gaze-Aware Task Progression Detection Framework for Human-Robot Interaction Using RGB Cameras

Linlin Cheng, Koen V. Hindriks, Artem V. Belopolsky

RA-L 2026

In human–robot interaction (HRI), detecting a human's gaze helps robots interpret user attention and intent. However, most gaze detection approaches rely on specialized eye-tracking hardware, limiting deployment in everyday set- tings. Appearance-based gaze estimation methods remove this dependency

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2025

Beware of the Tablet: A Dominant Distractor in Human-Robot Interaction

Linlin Cheng, Artem V. Belopolsky, Mark de Bruijn, Koen V. Hindriks

IROS 2025

The present study aims at investigating how humans engage with common communication modalities—speech, tablet, and gesture—when interacting with a humanoid robot. To explore this, we designed a live interaction experiment using a congruence paradigm, where participants engaged with a robot presentin

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