Autonomous knowledge acquisition based on active learning: Application to humanoid robots in indoor environment

In this paper, we present a cognitive system allowing a robot to discover the world, in which it evolves, and to learn autonomously high level knowledge about it from low level sensor data. The learning is accomplished both by observation and by an active interaction with a human, in which the robot is the actor of the learning. We first describe the system in a general manner and we provide experimental results on a humanoid robot in a real-world environment using everyday objects. We show, that our approach allows a humanoid robot to learn successfully without negative input and from small number of samples.