An Experiment in Morphological Development for Learning ANN Based Controllers

While control architectures and other information processing approaches are certainly important for robotic intelligence, our understanding of how this intelligence comes about has expanded in the last decades to include the morphology of the robot and its environment, as well as their mutual interactions [1]-[3], The field of Artificial Embodied Intelligence (AEI) [4], [5], which postulates robot intelligence as the result of the interaction between brain, morphology and the environment the robot must operate in, is growing. Currently, this view on the emergence of intelligence has expanded and now it tries to address the fact that intelligent systems must be able to operate in sequences of environments that are generally unknown at design time [6]. In other words, we are facing open-ended learning problems and, by definition, these types of problems imply that robots cannot be completely defined at design time, as, at that time, we do not know what skills the robot will require in order to achieve its purpose. In fact, not even the goals that need achieving are known. Developmental Robotics (DR) [7] is one of the approaches proposed to try to address these issues.