Bio-Inspired Object Reference Recognition in Human-Robot Interaction under Ambiguous Non-Verbal Cues

The object reference recognition task in human- robot interaction (HRI) consists of identifying the object to which a human is referring, based on communicative cues, including gaze and pointing, which is particularly challenging under ambiguous non-verbal behavior. This paper proposes a bio-inspired multimodal fusion algorithm to enable robots to recognize object references based on human gaze and pointing gestures. The proposed method integrates and encodes sensory inputs into a dynamic neural field, allowing the robot to adaptively resolve ambiguities in object refer- encing. The model was evaluated in an experimental setting where the participants interacted with a Furhat robot. The results showed that the system identified referenced objects with higher accuracy when both gaze and pointing cues were combined. Additionally, subjective evaluations using the Godspeed questionnaire indicated that participants perceived the robot more favorably when it engaged in joint attention behaviors. These results highlight the potential of dynamic neural models in improving intuitive and seamless HRI by addressing non-verbal ambiguity in shared workspaces. Fu- ture work will explore improved gaze-tracking techniques and closed-loop interaction models to enhance system robustness and adaptability.