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COgNiTive Architecture for Collaborative Technologies

The aim of this unit is to overcome the limitations of current human-machine co-working, where often machines and humans just work in the same space, to obtain a novel form of collaboration, in which the two partners can actually perform joint actions, establish mutual understanding and achieve real cooperation. To this aim, it is crucial to endow robots with the same degree of predictivity and intuition that characterizes humans, enabling them to understand and adapt to the other’s feelings, goals and needs. To achieve this goal, we focus on the investigation of the human perceptual, motor, and cognitive skills supporting efficient interaction. In particular, we also exploit robots as unique measurement tools for the systematic study of social interaction.  The research is conducted within the framework of the ERC StG project wHiSPER – Investigating Human Shared Perception with Robots.

The research activity is articulated into two branches

  • The investigation of the mechanisms supporting mutual understanding in human-human and human-robot interaction, with a focus on identifying the minimal verbal and non-verbal signals necessary to enable intuitive communication;
  • The implementation on the iCub humanoid robot platform of the derived models, to validate the theories, and bring forward a new generation of adaptive technologies, able to support and assist non-expert users.

Openings

IIT Publications List

Pubblicazioni
2021
Barros P.A.D., Tanevska A., Sciutti A.
Affect-Aware Learning for Social Robots
UMAP 2021 - Adjunct Publication of the 29th ACM Conference on User Modeling, Adaptation and Personalization, pp. 130-132
2021
Di Cesare G., Pelosi A., Aresta S.M., Lombardi G., Sciutti A.
Affective Contagion: How Attitudes Expressed by Others Influence Our Perception of Actions
Frontiers in Human Neuroscience, vol. 15
2021
Galvao Y.M., Portela L., Ferreira J., Barros P., De Araujo Fagundes O.A., Fernandes B.J.T.
A Framework for Anomaly Identification Applied on Fall Detection
IEEE Access, vol. 9, pp. 77264-77274
2021
Garello L., Lastrico L., Mastrogiovanni F., Sciutti A., Noceti N., Rea F.
A Generative Model Towards Conditioned Robotic Object Manipulation
3rd Italian Conference in Robotics and Intelligent Machines (I-RIM)
2021
Galvao Y.M., Ferreira J., Albuquerque V.A., Barros P., Fernandes B.J.T.
A multimodal approach using deep learning for fall detection
Expert Systems with Applications, vol. 168