Harold H. Chaput. 2004.
The Constructivist Learning Architecture:
A Model of Cognitive
Development for Robust Autonomous Robots.
Ph.D. thesis, Computer Science Department, University of Texas at Austin.
Technical Report TR-04-34, August 2004.
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 would allow 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 model 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 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. This 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
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