Developmental Robotics: From Babies to Robots (Intelligent by Angelo Cangelosi, Matthew Schlesinger

By Angelo Cangelosi, Matthew Schlesinger

Contributor note: ahead via Linda B. Smith

Developmental Robotics is a collaborative and interdisciplinary method of robotics that's at once encouraged through the developmental ideas and mechanisms saw in children's cognitive improvement. It builds at the concept that the robotic, utilizing a collection of intrinsic developmental ideas regulating the real-time interplay of its physique, mind, and setting, can autonomously collect an more and more advanced set of sensorimotor and psychological services. This quantity, drawing on insights from psychology, computing device technology, linguistics, neuroscience, and robotics, deals the 1st accomplished evaluate of a quickly transforming into field.

After delivering a few crucial historical past details on robotics and developmental psychology, the booklet seems intimately at how developmental robotics versions and experiments have tried to achieve a number of behavioral and cognitive services. The examples in those chapters have been selected as a result of their direct correspondence with particular concerns in baby psychology learn; each one bankruptcy starts with a concise and obtainable evaluate of proper empirical and theoretical findings in developmental psychology. The chapters hide intrinsic motivation and interest; motor improvement, interpreting either manipulation and locomotion; perceptual improvement, together with face reputation and notion of area; social studying, emphasizing such phenomena as joint awareness and cooperation; language, from phonetic babbling to syntactic processing; and summary wisdom, together with types of quantity studying and reasoning suggestions. Boxed textual content bargains technical and methodological info for either psychology and robotics experiments.

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Additional info for Developmental Robotics: From Babies to Robots (Intelligent Robotics and Autonomous Agents)

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3 Combination of feature extraction for salience map generation in Schlesinger, Amso, and Johnson 2007. See chapter 4 for more details on this model. , elimination of thin lines and thickening of thick lines). Edge detection requires the identification of the points in an image at which the image brightness changes sharply. Smoothing is typically done with the method of convolution, in other words, the sliding of an averaging window and summing up of the pixel intensity numbers within the center of the sliding window.

Riesenhuber and Poggio 1999; Serre and Poggio 2010; see Borisyuk et al. 2009, for a robotic application). These do not perform hand-defined analyses of the image features with predefined filtering methods. Rather, they use neural computation algorithms inspired by the physiology of the visual system. Neural networks process an image using a hierarchical processing architecture inspired by the way the visual cortex hierarchically processes visual stimuli from the primary cortex V1-V4 (with selectivity for stimulus orientation, size, depth, and motion direction) to the anterior inferotemporal (AIT) cortex (for object representation).

Again, in the example of Piaget’s four stages, the mental schemas used in each stage are qualitatively different, as they are the results of accommodation processes that change and adapt the schema to new knowledge representations and operations. Another wellknown developmental theory based on qualitative changes during development is the Representational-Redescription Model of Karmiloff-Smith (1995). Although KarmiloffSmith explicitly avoids the definition of age-determined stage models, as in Piaget, her model assumes four levels of development going from the use of implicit representation to different levels of explicit knowledge-representation strategies.

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