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Performing joint interaction requires constant mutual monitoring of ownactions and their effects on the other's behaviour. Such an action-effectmonitoring is boosted by social cues and might result in an increasing sense ofagency. Joint actions and joint attention are strictly correlated and both ofthem contribute to the formation of a precise temporal coordination. Inhuman-robot interaction, the robot's ability to establish joint attention witha human partner and exploit various social cues to react accordingly is acrucial step in creating communicative robots. Along the social component, aneffective human-robot interaction can be seen as a new method to improve andmake the robot's learning process more natural and robust for a given task. Inthis work we use different social skills, such as mutual gaze, gaze following,speech and human face recognition, to develop an effective teacher-learnerscenario tailored to visual object learning in dynamic environments.Experiments on the iCub robot demonstrate that the system allows the robot tolearn new objects through a natural interaction with a human teacher inpresence of distractors.

iCub Knows Where You Look: Exploiting Social Cues for Interactive Object Detection Learning