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            <title xml:lang="en">Bio-Inspired Object Reference Recognition in Human-Robot Interaction under Ambiguous Non-Verbal Cues</title>
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                <term xml:lang="en">Object referentiality</term>
                <term xml:lang="en">Bio-inspired AI</term>
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              <p>Object reference recognition in human-robot interaction (HRI) is a challenging research problem, particularly under ambiguous non-verbal cues. This paper proposes a bio-inspired multi-modal 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 referencing. The model was evaluated in an experimental setting where participants interacted with a humanoid robot, Furhat, in an object-referencing task. Results demonstrated that the system effectively identified referenced objects with significantly 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. The findings highlight the potential of dynamic neural models in improving intuitive and seamless HRI by addressing non-verbal ambiguity in shared workspaces. Future work will explore improved gaze-tracking techniques and closed-loop interaction models to enhance system robustness and adaptability</p>
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