Titlebook: Computational and Robotic Models of the Hierarchical Organization of Behavior; Gianluca Baldassarre,Marco Mirolli Book 2013 Springer-Verla

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Analogy

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Justin L. Balsor,Kathryn M. Murphyis area. We present a series of hierarchical motor models and relate these to various areas of brain function. This is followed by a discussion of the limitations of these models and directions for future research.

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Self-Organized Functional Hierarchy Through Multiple Timescales: Neuro-dynamical Accounts for Behaviynamics and physical actions, which could provide us new insights to understand nontrivial brain mechanisms. Those robot experiments successfully showed us how a set of behavior primitives can be learned with distributed neural activity and how functional hierarchy can be developed for manipulating these primitives in a compositional manner.

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Autonomous Representation Learning in a Developing Agentus learning of representations in a developing agent, and we demonstrate how these principles can be embodied in an algorithm. In a simulated environment with realistic physics, we show that an agent can use these principles to autonomously learn useful representations and effective hierarchical actions.

ADORE

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A Developmental Framework for Cumulative Learning Robotss described and illustrated with results from experiments. We discuss how this approach is grounded in the topics of sensory-motor abstraction, intrinsic motivation (as novelty), and staged learning, and our belief that robotics can learn much from infant psychology.

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