Intention inference learning through the interaction with a caregiver

Humans are experts in understanding others´ intention. This capacity is essential to interact and collaborate with others. Thus, providing a robot with this capacity is a key improvement for further developments in socially interactive robotics. This work exploits a mirroring scenario between aa caregiver and a robot to develop an intention inference capacity without providing a priori knowledge to the robot. A sensory-motor architecture is used to learn autonomously associations between the robot´s internal states and its perception.