From creative cognitive learning to adaptable artificial system design

Background: Over the last decade, a number of researchers have suggested a developmental perspective on AI and robotics. The ultimate shared goal among them seems to be the idea of bootstrapping high-level cognition through a process in which the agent interacts with a real physical environment over extended periods of time [2]. These studies generated epigenetic robotics, a new AI/ robotics field which includes the two-fold goal of understanding biological systems by the interdisciplinary integration between social/life and engineering sciences and, simultaneously, that of enabling robots and other artificial systems to autonomously develop skills for any particular environment (instead of programming them to solve particular goals for a specific environment). Interdisciplinary theory and empirical evidence are used to inform epigenetic robotic models, and these models can be used as theoretical tools to make experimental predictions in developmental psychology and other disciplines studying cognitive development in living systems. One of the fundamental methodological assumptions is that cognition is embodied, which means that it arises from bodily interactions with the real world[1]. The next logical step along the road towards truly autonomous robots that can dive in unpredictable environments is to investigate how one might design robots that are capable of `growing up' through experience. A living artifact grows up when its capabilities, abilities/knowledge, shift to a further level of complexity [3]. Following different psychological points of view, growing up implies: adaptation, change of functional meaning; increased complexity; enlargement of the internal knowledge map; abstraction and insight.