  Human emotions are expressed through multiple modalities, including verbal
and non-verbal information. Moreover, the affective states of human users can
be the indicator for the level of engagement and successful interaction,
suitable for the robot to use as a rewarding factor to optimize robotic
behaviors through interaction. This study demonstrates a multimodal human-robot
interaction (HRI) framework with reinforcement learning to enhance the robotic
interaction policy and personalize emotional interaction for a human user. The
goal is to apply this framework in social scenarios that can let the robots
generate a more natural and engaging HRI framework.
