Abstract
In general, traditional machine learning algorithms typically employ task-specific methods and only the parameters pre-determined by the human programmer are updated. These methods often fail to respond to the dynamically changing states of the uncontrolled environments. Additionally, such methods may not represent a developmental entity, such as a human mind. In contrast, an open-ended developmental robot system can learn simple behaviors and buildup more complex behaviors by utilizing the previously learned behaviors. In this chapter, we propose a basic framework for visual learning tasks that integrates a perceptual system into a biologically inspired working memory system. A main objective of this research is to provide a general framework for developmental learning and to investigate how well a neuro-computational PFC working memory model performs on a robotic platform in a real-world environment with complex tasks. Experiments conducted show impressive results.
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Wang, X., Wang, X., Wilkes, D.M. (2020). A Developmental Robotic Paradigm for Mobile Robot Navigation in an Indoor Environment. In: Machine Learning-based Natural Scene Recognition for Mobile Robot Localization in An Unknown Environment. Springer, Singapore. https://doi.org/10.1007/978-981-13-9217-7_14
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DOI: https://doi.org/10.1007/978-981-13-9217-7_14
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