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Computer Science > Robotics

arXiv:2207.13552 (cs)
[Submitted on 27 Jul 2022 (v1), last revised 21 Dec 2022 (this
version, v2)]

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

Authors:Maria Lombardi, Elisa Maiettini, Vadim Tikhanoff, Lorenzo
Natale
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    Abstract: Performing joint interaction requires constant mutual
    monitoring of own actions and their effects on the other's
    behaviour. Such an action-effect monitoring is boosted by social
    cues and might result in an increasing sense of agency. Joint
    actions and joint attention are strictly correlated and both of
    them contribute to the formation of a precise temporal
    coordination. In human-robot interaction, the robot's ability to
    establish joint attention with a human partner and exploit
    various social cues to react accordingly is a crucial step in
    creating communicative robots. Along the social component, an
    effective human-robot interaction can be seen as a new method to
    improve and make the robot's learning process more natural and
    robust for a given task. In this work we use different social
    skills, such as mutual gaze, gaze following, speech and human
    face recognition, to develop an effective teacher-learner
    scenario tailored to visual object learning in dynamic
    environments. Experiments on the iCub robot demonstrate that the
    system allows the robot to learn new objects through a natural
    interaction with a human teacher in presence of distractors.

Subjects:          Robotics (cs.RO)
Cite as:           arXiv:2207.13552 [cs.RO]
                   (or arXiv:2207.13552v2 [cs.RO] for this version)
                   https://doi.org/10.48550/arXiv.2207.13552
                   Focus to learn more
                   arXiv-issued DOI via DataCite
Journal reference: IEEE International Conference on Humanoid Robots,
                   2022

Submission history

From: Elisa Maiettini [view email]
[v1] Wed, 27 Jul 2022 14:43:29 UTC (5,220 KB)
[v2] Wed, 21 Dec 2022 10:29:37 UTC (14,054 KB)
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