The project involves the collaborative effort of INESC-ID and ISCTE-IUL. This project investigates the role of emotions and adaptation in interactions between a robot and a group of users, contrasting to the typical one-robot one user paradigm in Human-Robot Interaction (HRI). It hypothesizes that if a robot can capture the dynamics of the affective interactions of a small group and is able to adapt its emotional behavior accordingly, users will be more willing to sustain the interaction with the robot for longer periods of time, bringing us closer to the establishment of sustainable and engaging long-term interactions. This experimental project employs two machine learning techniques: (1) online learning by regret minimization where the robot has a set of pre-determined high-level emotional behaviors that indicate how the robot should respond, depending on the situation, and (2) reinforcement learning where the robot is endowed with a repertoire of lower-level response primitives and slowly adapts its responses in a context-specific manner. The experiments will be conducted outside laboratory conditions with young adults.