Generation of acceptable actions using imitation learning, intention recognition, and cognitive control

This paper proposes an approach to enable a robot to learn social skills to interact with humans in social settings. Our approach is based on integrating a cognitive control architecture with imitation learning and human intention recognition. The originally developed cognitive control architecture was expanded to include behavior generalization, behavior generation, and human intention recognition. Our approach provides a framework for a robot to be able to estimate human intention through common features of human gestures through observation couple with past experiences stored in the long-term memory in the form of learned social knowledge, and to cognitively generate appropriate motion behaviors or modify current behavior using arm and hand. Key components of our approach are a probabilistic arm and hand gesture recognition and cognitive control modules which integrate the gesture recognition with human intention recognition and motion behavior generation. Several experiments were carried out on humanoid robots to validate the proof of concept.