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.

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PhD Thesis, Department of Computer Sciences, The University of Texas at Austin, August 2004. Also Technical Report TR-04-34.

@phdthesis{chaput:phd04, title={The Constructivist Learning Architecture: A Model of Cognitive Development for Robust Autonomous Robots}, author={Harold H. Chaput}, month={August}, school={Department of Computer Sciences, The University of Texas at Austin}, note={Also Technical Report TR-04-34}, url="http://www.cs.utexas.edu/users/ai-lab/?cla", year={2004} }