Perception and action and the notion of symbolic representation are therefore core issues to be addressed in cognitive robotics.
Target robotic cognitive capabilities include perception processing, attention allocation, anticipation, planning, complex motor coordination, reasoning about other agents and perhaps even about their own mental states.
It is often a challenge to transform imitation information from a complex scene into a desired motor result for the robot.
A more complex learning approach is "autonomous knowledge acquisition": the robot is left to explore the environment on its own.
[3] These algorithms generally involve breaking sensory input into a finite number of categories and assigning some sort of prediction system (such as an Artificial Neural Network) to each.