For a long time, robots were assigned to repetitive tasks such as industrial chains, where social skills played a secondary role. However, as next-generation robots are designed to interact and collaborate with humans, it becomes ever more important to endow them with social competencies. Humans use several explicit and implicit cues, like gaze, facial expression, and gestures to communicate. To understand and acquire these skills, social interaction during infancy plays a crucial role. Babies already at birth show social skills and continue to learn and develop them during all childhood. As robots will interact more frequently with us, with the goal of becoming our social companions helping us in different tasks, they should also learn these abilities. Deep learning algorithms have reached state-of-the-art results in different tasks, from objects recognition/detection to speech recognition. They demonstrated to be powerful tools to address complex problems, being valid candidates to make robots learn social skills. However, most of the achievements were reached using a supervised-learning approach, where large annotated datasets are available. This requires human supervision in both collecting and annotating the data, which in robotic applications can be problematic. Indeed, those networks, when applied in robots, can suffer from a drop in performance and need to be fine-tuned. Consequently, the annotation process has to be repeated to deal with the inherent dynamicity of robots, which is time-consuming and limits the autonomy of robots in their learning. Robots, being embodied, have access to a continuous stream of data thanks to their different sensors. Thus, instead of relying on human supervision to annotate their data, they should learn in a self-supervised way, similarly to babies in their early development. Advancements in neuroscience and psychology give some insights into how babies learn and develop social skills. For example, attentional mechanisms and multi-modal experience play an important role to ...


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    Titel :

    Self-supervised solutions for developmental learning with the humanoid robot iCub.



    Erscheinungsdatum :

    2021-06-09


    Anmerkungen:

    doi:10.15167/gonzalez-jonas-pierre-gustavo_phd2021-06-09



    Medientyp :

    Hochschulschrift


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629



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