Human intelligence is acquired through a prolonged period of maturation and growth during which a single fertilized egg first turns into an embryo, then grows into a newborn baby, and eventually becomes an adult individual—which, typically before growing old and dying, reproduces. The developmental process is inherently robust and flexible, and biological organisms show an amazing ability during their development to devise adaptive strategies and solutions to cope with environmental changes and guarantee their survival. Because evolution has selected development as the process through which to realize some of the highest known forms of intelligence, it is plausible to assume that development is mechanistically crucial to emulate such intelligence in human-made artifacts.
Developmental robotics (also known as epigenetic or ontogenetic robotics) is a highly interdisciplinary subfield of robotics in which ideas from artificial intelligence, developmental psychology, neuroscience, and dynamical systems theory play a pivotal role in motivating the research (Asada et al., 2001; Lungarella et al., 2003; Weng et al., 2001; Zlatev & Balkenius, 2001). Developmental robotics aims to model the development of increasingly complex cognitive processes in natural and artificial systems and to understand how such processes emerge through physical and social interaction. The idea is to realize artificial cognitive systems not by simply programming them to solve a specific task, but rather by initiating and maintaining a developmental process during which the systems interact with their physical environments (i.e. through their bodies or tools), as well as with their social environments (i.e. with people or other robots). Cognition, after all, is the result of a process of self-organization (spontaneous emergence of order) and co-development between a developing organism and its surrounding environment. Although some researchers use simulated environments and computational models (e.g. Mareschal et al., 2007), often robots are employed as testing platforms for theoretical models of the development of cognitive abilities – the rationale being that if a model is instantiated in a system interacting with the real world, a great deal can be learned about its strengths and potential flaws (Fig. 1). Unlike evolutionary robotics which operates on phylogenetic time scales and populations of many individuals, developmental robotics capitalizes on “short” (ontogenetic) time scales and single individuals (or small groups of individuals).
Key Terms in this Chapter
Embodiment: Refers to the fact that intelligence requires a body, and cannot merely exist in the form of an abstract algorithm.
Degrees of freedom problem: The problem of learning how to control a system with a very large number of degrees of freedom (also known as Bernstein’s problem).
Semiotic Dynamics: Field that studies how meaningful symbolic structures originates, spreads, and evolve over time within populations, by combining linguistics and cognitive science with theoretical tools from complex systems and computer science.
Emergence: A process where phenomena at a certain level arise from interactions at lower levels. The term is sometimes used to denote a property of a system not contained in any one of its parts.
Bootstrapping: Designates the process of starting with a minimal set of functions and building increasingly more functionality in a step by step manner on top of structures already present in the system.
Scaffolding: Encompasses all kinds of external support and aids that simplify the learning of tasks and the acquisition of new skills.
Adaptation: Refers to particular adjustments that organisms undergo to cope with environmental and morphological changes. In biology one can distinguish four types of adaptation: evolutionary, physiological, sensory, and learning.