Currently most cognitive and social robots only operate in the here and now, the ALIZ-E project aims to move human-robot interaction from the range of minutes to the range of days. The project will develop the theory and practice behind embodied cognitive robots capable of maintaining believable any-depth affective interactions with a young user over an extended and possibly discontinuous period of time.
Scientific and technological goals
- Prolonged human-robot interaction over a range of days instead of in the here and now.
- Robotic companions in child-robot interaction. Different from adult-robot interaction, more promising applications.
- Robust “any-depth" interaction. Robustness against low-quality perception and interpretation.
- Out of the lab into the real world: the robot will be evaluated in paediatrics department.
- Long-term memory and self-sustained long-term interaction. Key to long-term interaction is having a personalised adaptive memory storing experiences and interaction episodes.
- Analysis and synthesis of emotion and affect in human-robot interaction.
- Pervasive machine learning and adaptation. Learning experiences will be unstructured. Learning will rely on an array of different approaches.
- Cloud computing as computational resource on autonomous systems.
Science and technology
- The robots will use a distributed model of long-term memory, which acts as a switch board for other cognitive modalities.
- The robots will rely on adaptive and sustainable non-verbal interaction, taking an embodied perspective to affective interaction.
- The user and task modelling will be adaptive, as the robot adapts its behaviour to different user profiles and employs user-specific strategies to achieve a goal.
- Verbal interaction aimed at long-term interaction will be strongly coupled with non-verbal interaction.
- Evaluation will involve young users outside a lab environment. The main evaluation site will be the Hospital San Rafaelle in Milan, Italy.
- The integration of cognitive components will be based on cloud computing for embedded systems.
