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== 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 centre 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
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| last1 = Lungarella | first1 = M. | last2 = Metta | first2 = G. | last3 = Pfeifer | first3 = R. | first4 = G. | last4 = Sandini | date = 2003 | title = Developmental robotics: a survey | id = {{citeseerx|10.1.1.83.7615}} | journal = Connection Science | issue = 15 | pages = 151–190 }}</ref><ref name="Asada09">{{cite journal
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▲Asada, M., Hosoda, K., Kuniyoshi, Y., Ishiguro, H., Inui, T., Yoshikawa, Y., Ogino, M. and C. Yoshida (2009) [http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=4895715&url=http%3A%2F%2Fieeexplore.ieee.org%2Fiel5%2F4563672%2F5038478%2F04895715.pdf%3Farnumber%3D4895715 Cognitive developmental robotics: a survey]. IEEE Transactions on Autonomous Mental Development, Vol.1, No.1, pp.12--34.</ref><ref name="Oudeyer10">
▲[[Pierre-Yves Oudeyer|Oudeyer, P-Y.]] (2010) [http://www.pyoudeyer.com/IEEETAMDOudeyer10.pdf On the impact of robotics in behavioral and cognitive sciences: from insect navigation to human cognitive development], IEEE Transactions on Autonomous Mental Development, 2(1), pp. 2--16.</ref>
Because the concept of adaptive intelligent machine is central to developmental robotics, is 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 traditional machine learning because it targets task- independent self-determined learning rather than task-specific inference over "spoon fed human-edited sensori data" (Weng et al., 2001). 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:
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# 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
== Research directions ==
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The sensorimotor and social spaces in which humans and robot live are so large and complex that only a small part of potentially learnable skills can actually be explored and learnt within a life-time. Thus, mechanisms and constraints are necessary to guide developmental organisms in their development and control of the growth of complexity. There are several important families of these guiding mechanisms and constraints which are studied in developmental robotics, all inspired by human development:
# Motivational systems, generating internal reward signals that drive exploration and learning, which can be of two main types:
#* extrinsic motivations push robots/organisms to maintain basic specific internal properties such as food and water level, physical integrity, or light (for e.g. in phototropic systems);
#* intrinsic motivations push robot to search for novelty, challenge, compression or learning progress per se, thus generating what is sometimes called curiosity-driven learning and exploration, or alternatively active learning and exploration;
#Social guidance: as humans learn a lot by interacting with their peers, developmental robotics investigates mechanisms which can allow robots to participate to human-like social interaction. By perceiving and interpreting social cues, this may allow robots both to learn from humans (through diverse means such as imitation, emulation, stimulus enhancement, demonstration, etc. ...) and to trigger natural human pedagogy. Thus, social acceptance of developmental robots is also investigated;
# Statistical inference biases and cumulative knowledge/skill reuse: biases characterizing both representations/encodings and inference mechanisms can typically allow considerable improvement of the efficiency of learning and are thus studied. Related to this, mechanisms allowing to infer new knowledge and acquire new skills by reusing previously learnt structures is also an essential field of study;
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During biological epigenesis, morphology is not fixed but rather develops in constant interaction with the development of sensorimotor and social skills. The development of morphology poses obvious practical problems with robots, but it may be a crucial mechanism that should be further explored, at least in simulation, such as in morphogenetic robotics.
Similarly, in biology, developmental mechanisms (operating at the ontogenetic time scale) strongly interact with evolutionary mechanisms (operating at the phylogenetic time scale) as shown in the flourishing "evo- devo" scientific literature
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==Main Journals==
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=== Technical committees ===
*IEEE Technical committee on Autonomous Mental Development, http://www.icdl-epirob.org/amdtc
*IEEE Technical
===Academic institutions and researchers in the field ===
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