Fusion, propagation, and structuring in belief networks
Artificial Intelligence
Intelligence without representation
Artificial Intelligence
Made-up minds: a constructivist approach to artificial intelligence
Made-up minds: a constructivist approach to artificial intelligence
Catching Ourselves in the Act: Situated Activity, Interactive Emergence, Evolution, and Human Thought
Evolutionary Robotics: The Biology,Intelligence,and Technology
Evolutionary Robotics: The Biology,Intelligence,and Technology
The Computational Brain
Construction of Robot Intra-modal and Inter-modal Coordination Skills by Developmental Learning
Journal of Intelligent and Robotic Systems
Staged Competence Learning in Developmental Robotics
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Learning robotic hand-eye coordination through a developmental constraint driven approach
International Journal of Automation and Computing
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Biologically inspired robotics offers the promise of future autonomous devices that can perform significant tasks while coping with noisy, real-world environments. In order to survive for long periods we believe a developmental approach to learning is required and we are investigating the design of such systems inspired by results from developmental psychology. Developmental learning takes place in the context of an epigenetic framework that allows environmental and internal constraints to shape increasing competence and the gradual consolidation of control, coordination and skill. In this paper we describe the use of novelty and habituation as the motivation mechanism for a sensory-motor learning process. In our system, a biologically plausible habituation model is utilized and the effect of parameters such as habituation rate and recovery rate on the learning/development process is studied. We concentrate on the very early stages of development in this work. The learning process is based on a topological mapping structure which has several attractive features for sensory-motor learning. The motivation model was implemented and tested through a series of experiments on a working robot system with proprioceptive and contact sensing. Stimulated by novelty, the robot explored its egocentric space and learned to coordinate motor acts with sensory feedback. Experimental results and analysis are given for different parameter configurations, proprioceptive encoding schemes, and stimulus habituation schedules.