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WIREs Cogn Sci
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What do we learn about development from baby robots?

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Understanding infant development is one of the great scientific challenges of contemporary science. In addressing this challenge, robots have proven useful as they allow experimenters to model the developing brain and body and understand the processes by which new patterns emerge in sensorimotor, cognitive, and social domains. Robotics also complements traditional experimental methods in psychology and neuroscience, where only a few variables can be studied at the same time. Moreover, work with robots has enabled researchers to systematically explore the role of the body in shaping the development of skill. All told, this work has shed new light on development as a complex dynamical system. WIREs Cogn Sci 2017, 8:e1395. doi: 10.1002/wcs.1395 This article is categorized under: Computer Science > Robotics Psychology > Development and Aging
Robots can help us model and study the complex interactions among the brain, the body, and the environment during cognitive development. Here we see two open‐source robotic platforms used in laboratories. Being open‐source allows open science and better replicability by revealing experimental details. Built using 3D printing, the Poppy (right) allows fast and efficient exploration of various body morphologies and how they affect skill development, such as leg shape (see alternative morphologies on the right (a) and (b)). Left: ICub http://www.icub.org; Right: Poppy http://www.poppy‐project.org.
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Curiosity‐driven learning and self‐organization of developmental structure in the Playground Experiment. The robot in the center explores and learns to predict the effects of its actions, driven by an intrinsic motivation mechanism that drives it to focus on sensorimotor experiments that maximize learning progress/information gain. At the short time scale, this constitutes a model of curiosity‐driven attention pushing the robot to prefer sensorimotor contingencies of intermediate complexity, compatible with recent experiments studying informational preferences in infants. When running over an extended period of time, one observes the spontaneous self‐organization of developmental phases of increasing complexity, without an initial program that specifies these phases. For example, the robot learns on its own to grasp an object in front of it, and later on focuses on exploring how to produce vocalizations that elicit reactions in another robot.
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A robot that walks but does not have a brain! It has neither electric power nor computer control. The steps it takes are spontaneously generated through the physical interaction between its structure and gravity. Such mechanical experiments allow to characterize concretely how self‐organization of behavioral structure can arise in physical dynamical systems such as proposed by the dynamical approach to development. Source: http://dyros.snu.ac.kr/concept‐of‐passive‐dynamic‐walking‐robot/.
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The architecture of termite nests is the self‐organized result of local interactions among thousands of insects. None of these insects has a map of the architecture. Computer simulations contributed to understanding this process. Today, models in developmental robotics are used similarly to study how local short term mechanisms can give rise to macro and long‐term behavioral and cognitive developmental structure. Source: http://commons.wikimedia.org/wiki/File:Termite%27s_nest.jpg.
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