The constructivist learning architecture: A model of cognitive development for robust autonomous robots

Autonomous robots are used more and more in remote and inaccessible places where they cannot be easily repaired if damaged or improperly programmed. A system is needed that allows these robots to repair themselves by recovering gracefully from damage and adapting to unforeseen changes. Newborn infants employ such a system to adapt to a new and dynamic world by building a hierarchical representation of their environment. This model allows them to respond robustly to changes by falling back to an earlier stage of knowledge, rather than failing completely. A computational model that replicates these phenomena in infants would afford a mobile robot the same adaptability and robustness that infants have. This dissertation presents such a model, the Constructivist Learning Architecture (CLA), that builds a hierarchical knowledge base using a set of interconnected self-organizing learning modules. The dissertation then demonstrates that CLA (1) replicates current studies in infant cognitive development, (2) builds sensorimotor schemas for robot control, (3) learns a goal-directed task from delayed rewards, and (4) can fall back and recover gracefully from damage. CLA is a new approach to robot control that allows robots to recover from damage or adapt to unforeseen changes in the environment. CLA is also a new approach to cognitive modeling that can be used to better understand how people learn for their environment in infancy and adulthood.