  Learning from Demonstration (LfD) has been established as the dominant
paradigm for efficiently transferring skills from human teachers to robots. In
this context, the Federated Learning (FL) conceptualization has very recently
been introduced for developing large-scale human-robot collaborative
environments, targeting to robustly address, among others, the critical
challenges of multi-agent learning and long-term autonomy. In the current work,
the latter scheme is further extended and enhanced, by designing and
integrating a novel user profile formulation for providing a fine-grained
representation of the exhibited human behavior, adopting a Deep Learning
(DL)-based formalism. In particular, a hierarchically organized set of key
information sources is considered, including: a) User attributes (e.g.
demographic, anthropomorphic, educational, etc.), b) User state (e.g. fatigue
detection, stress detection, emotion recognition, etc.) and c)
Psychophysiological measurements (e.g. gaze, electrodermal activity, heart
rate, etc.) related data. Then, a combination of Long Short-Term Memory (LSTM)
and stacked autoencoders, with appropriately defined neural network
architectures, is employed for the modelling step. The overall designed scheme
enables both short- and long-term analysis/interpretation of the human behavior
(as observed during the feedback capturing sessions), so as to adaptively
adjust the importance of the collected feedback samples when aggregating
information originating from the same and different human teachers,
respectively.
