Editing Developmental robotics (section)

== Background ==

Can a robot learn like a child? Can it learn a variety of new skills and new knowledge unspecified at design time and in a partially unknown and changing environment? How can it discover its body and its relationships with the physical and social environment? How can its cognitive capacities continuously develop without the intervention of an engineer once it is "out of the factory"? What can it learn through natural social interactions with humans? These are the questions at the center of developmental robotics. Alan Turing, as well as a number of other pioneers of cybernetics, already formulated those questions and the general approach in 1950,<ref name="Turing50">{{cite journal
| last = Turing | first = A.M. | date = 1950 | url = http://www.csee.umbc.edu/courses/471/papers/turing.pdf | title = Computing machinery and intelligence | journal = Mind | publisher = LIX | issue = 236 | pages = 433–460 | volume=LIX|  doi = 10.1093/mind/LIX.236.433 }}</ref>
but it is only since the end of the 20th century that they began to be investigated systematically.<ref name="Lungarella03">{{cite journal
| last1 = Lungarella | first1 = M. | last2 = Metta | first2 = G. | last3 = Pfeifer | first3 = R. | first4 = G. | last4 = Sandini | date = 2003 | title =  Developmental robotics: a survey | citeseerx = 10.1.1.83.7615 | journal = Connection Science | volume = 15 | issue = 4 | pages = 151–190 | doi=10.1080/09540090310001655110| s2cid = 1452734 }}</ref><ref name="Asada09">{{cite journal
| last1 = Asada | first1 = M. | last2 = Hosoda | first2 = K. | last3 = Kuniyoshi | first3 = Y. | last4 = Ishiguro | first4 = H. | last5 = Inui | first5 = T. | last6 = Yoshikawa | first6 = Y. | last7 = Ogino | first7 = M. | first8 = C. | last8 = Yoshida | date = 2009 | title = Cognitive developmental robotics: a survey | journal = IEEE Transactions on Autonomous Mental Development | volume = 1 | issue = 1 | pages = 12–34 | doi=10.1109/tamd.2009.2021702| s2cid = 10168773 }}</ref><ref name="Oudeyer10">{{cite journal
| authorlink1 = Pierre-Yves Oudeyer | last1 = Oudeyer | first1 = P-Y. | date = 2010 | url = http://www.pyoudeyer.com/IEEETAMDOudeyer10.pdf | title = On the impact of robotics in behavioral and cognitive sciences: from insect navigation to human cognitive development | journal = IEEE Transactions on Autonomous Mental Development | volume = 2 | issue = 1 | pages = 2–16 | doi=10.1109/tamd.2009.2039057| s2cid = 6362217 }}</ref>

Because the concept of adaptive intelligent machines is central to developmental robotics, it has relationships with fields such as artificial intelligence, machine learning, [[cognitive robotics]] or [[computational neuroscience]]. Yet, while it may reuse some of the techniques elaborated in these fields, it differs from them from many perspectives. It differs from classical artificial intelligence because it does not assume the capability of advanced symbolic reasoning and focuses on embodied and situated sensorimotor and social skills rather than on abstract symbolic problems. It differs from cognitive robotics because it focuses on the processes that allow the formation of cognitive capabilities rather than these capabilities themselves. It differs from computational neuroscience because it focuses on functional modeling of integrated architectures of development and learning. More generally, developmental robotics is uniquely characterized by the following three features:
# It targets task-independent architectures and learning mechanisms, i.e. the machine/robot has to be able to learn new tasks that are unknown by the engineer;
# It emphasizes open-ended development and lifelong learning, i.e. the capacity of an organism to acquire continuously novel skills. This should not be understood as a capacity for learning "anything" or even “everything”, but just that the set of skills that is acquired can be infinitely extended at least in some (not all) directions;
# The complexity of acquired knowledge and skills shall increase (and the increase be controlled) progressively.

Developmental robotics emerged at the crossroads of several research communities including embodied artificial intelligence, enactive and dynamical systems cognitive science, connectionism. Starting from the essential idea that learning and development happen as the self-organized result of the dynamical interactions among brains, bodies and their physical and social environment, and trying to understand how this self-organization can be harnessed to provide task-independent lifelong learning of skills of increasing complexity, developmental robotics strongly interacts with fields such as developmental psychology, developmental and cognitive neuroscience, developmental biology (embryology), evolutionary biology, and [[cognitive linguistics]]. As many of the theories coming from these sciences are verbal and/or descriptive, this implies a crucial formalization and computational modeling activity in developmental robotics. These computational models are then not only used as ways to explore how to build more versatile and adaptive machines but also as a way to evaluate their coherence and possibly explore alternative explanations for understanding biological development.<ref name="Oudeyer10" />