When humans interact with each other, collaborating on a shared activity or chatting, they are able to tell whether their interaction is going well or not and if they observe that its quality is deteriorating, they can adapt their behavior or invite their partner to act in order to improve it. A robot endowed with the ability to evaluate the quality of its interaction with its human partners, will have the opportunity to perform better since it will be better informed for its decision making processes. We propose metrics to be integrated in a cognitive and collaborative robot in order to measure in real-time the quality of an interaction (QoI). This permanent evaluation process has been implemented and tested within the high-level controller of an entertainment robot. A first demonstration shows the ability of the scheme to compute QoI for a guiding task and exhibit significant differences between its performance in interaction with a fully compliant human, a human confused by the course of action and a noncooperative one. This paper is an extension and further refinement of work originally reported in [28].