  Socially aware robots should be able, among others, to support fluent
human-robot collaboration in tasks that require interdependent actions in order
to be solved. Towards enhancing mutual performance, collaborative robots should
be equipped with adaptation and learning capabilities. However, co-learning can
be a time consuming procedure. For this reason, transferring knowledge from an
expert could potentially boost the overall team performance. In the present
study, transfer learning was integrated in a deep Reinforcement Learning (dRL)
agent. In a real-time and real-world set-up, two groups of participants had to
collaborate with a cobot under two different conditions of dRL agents; one that
was transferring knowledge and one that did not. A probabilistic policy reuse
method was used for the transfer learning (TL). The results showed that there
was a significant difference between the performance of the two groups; TL
halved the time needed for the training of new participants to the task.
Moreover, TL also affected the subjective performance of the teams and enhanced
the perceived fluency. Finally, in many cases the objective performance metrics
did not correlate with the subjective ones providing interesting insights about
the design of transparent and explainable cobot behaviour.
