  The high request for autonomous and flexible HRI implies the necessity of
deploying Machine Learning (ML) mechanisms in the robot control. Indeed, the
use of ML techniques, such as Reinforcement Learning (RL), makes the robot
behaviour, during the learning process, not transparent to the observing user.
In this work, we proposed an emotional model to improve the transparency in RL
tasks for human-robot collaborative scenarios. The architecture we propose
supports the RL algorithm with an emotional model able to both receive human
feedback and exhibit emotional responses based on the learning process. The
model is entirely based on the Temporal Difference (TD) error. The architecture
was tested in an isolated laboratory with a simple setup. The results highlight
that showing its internal state through an emotional response is enough to make
a robot transparent to its human teacher. People also prefer to interact with a
responsive robot because they are used to understand their intentions via
emotions and social signals.
