Our research aims at developing bio-inspired cognitive architectures for autonomous learning for robots. We integrate machine learning algorithms with facilitation mechanisms used by children, such as visual attention, to build computational frameworks for self-supervised learning. Indeed, several issues arise when applying established AI methodologies to data gathered in HRI frameworks. The problems are mostly caused by the scarcity of available data together with the diverse nature of the data stream and the inherent variability of HRI settings. To overcome this limitation, it is crucial to exploit the robot’s embodiment in the real world, i.e. the advantage of perceiving directly the world and continuously updating the sensorial data from humans and the environment. The open challenge is therefore to design frameworks aimed at autonomously and effectively managing the data captured by the robot during interactions with humans so that it can use this information to learn in an autonomous and self-supervised way. One way to let the robot autonomously label the data acquired is to rely on multimodal data (e.g., auditory and visual ones) where one modality helps to supervise the other.
The robot’s ability to organize its own sensorial experience and to retrieve it in future contexts is of utmost importance for personalized, long-term HRI scenarios.
- Gonzalez-Billandon, J., Grasse, L., Tata, M., Sciutti, A., & Rea, F. (2020). Self-supervised reinforcement learning for speaker localisation with the iCub humanoid robot. arXiv preprint arXiv:2011.06544.
- Gonzalez-Billandon, J., Belgiovine, G., Sciutti, A., Sandini, G., & Rea, F. (2021). Cognitive architecture aided by working-memory for self-supervised multi-modal humans recognition. arXiv preprint arXiv:2103.09072
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