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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="
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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 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" />
== Research directions ==
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# 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 (e.g. in phototropic systems);
#* [[intrinsic motivation (artificial intelligence) | 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
# 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;
#The properties of embodiment, including geometry, materials, or innate motor primitives/synergies often encoded as dynamical systems, can considerably simplify the acquisition of sensorimotor or social skills, and is sometimes referred as morphological computation. The interaction of these constraints with other constraints is an important axis of investigation;
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Among the strategies to explore to progress towards this target, the interaction between the mechanisms and constraints described in the previous section shall be investigated more systematically. Indeed, they have so far mainly been studied in isolation. For example, the interaction of intrinsically motivated learning and socially guided learning, possibly constrained by maturation, is an essential issue to be investigated.
Another important challenge is to allow robots to perceive, interpret and leverage the diversity of [[Multimodal_interaction|multimodal]] social cues provided by non-engineer humans during human-robot interaction. These capacities are so far, mostly too limited to allow efficient general-purpose teaching from humans.
A fundamental scientific issue to be understood and resolved, which applied equally to human development, is how compositionality, functional hierarchies, primitives, and modularity, at all levels of sensorimotor and social structures, can be formed and leveraged during development. This is deeply linked with the problem of the emergence of symbols, sometimes referred to as the "[[symbol grounding problem]]" when it comes to language acquisition. Actually, the very existence and need for symbols in the brain
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.
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Similarly, in biology, developmental mechanisms (operating at the ontogenetic time scale) interact closely with evolutionary mechanisms (operating at the phylogenetic time scale) as shown in the flourishing "[[evo-devo]]" scientific literature.<ref name="Muller07">{{cite journal
| last1 = Müller | first1 = G. B. | date = 2007 | title = Evo-devo: extending the evolutionary synthesis | journal = Nature Reviews Genetics | volume = 8 | issue = 12 | pages = 943–949 | doi=10.1038/nrg2219 | pmid=17984972| s2cid = 19264907 }}</ref>
However, the interaction of those mechanisms in artificial organisms, developmental robots, in particular, is still vastly understudied. The interaction of evolutionary mechanisms, unfolding morphologies and developing sensorimotor and social skills will thus be a highly stimulating topic for the future of developmental robotics.
==Main journals==
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=== Technical committees ===
*IEEE Technical Committee on Cognitive and Developmental Systems (CDSTC), previously known as IEEE Technical Committee on Autonomous Mental Development, [https://web.archive.org/web/20190822214757/https://cis.ieee.org/technical-committees/cognitive-and-developmental-systems-technical-committee]
*IEEE Technical Committee on Cognitive Robotics, https://www.ieee-ras.org/cognitive-robotics
*IEEE Technical Committee on Robot Learning, https://www.ieee-ras.org/robot-learning/
=== Academic institutions and researchers in the field ===
*[https://www.lucs.lu.se/lucs-robotics-group/ Lund University Cognitive Science - Robotics Group]
* [http://www.iub.edu/~cogdev/ Cognitive Development Lab, University of Indiana, US]
* [[Michigan State University]] – [http://www.cse.msu.edu/ei Embodied Intelligence Lab]
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* [[Bryn Mawr College]]'s [http://cs.brynmawr.edu/devrob/ Developmental Robotics Project]: research projects by faculty and students at Swarthmore and Bryn Mawr Colleges, Philadelphia, PA, USA
* [https://web.archive.org/web/20070222045437/http://eksl.isi.edu/cgi-bin/page.cgi?page=project-jean.html Jean Project]: Information Sciences Institute of the University of Southern California
* [http://www.nrl.navy.mil/aic/iss/aas/CognitiveRobots.php Cognitive Robotics (including Hide and Seek) at the Naval Research Laboratory] {{Webarchive|url=https://web.archive.org/web/20100808015544/http://www.nrl.navy.mil/aic/iss/aas/CognitiveRobots.php |date=August 8, 2010 }}
* [http://www-robotics.cs.umass.edu/index.php The Laboratory for Perceptual Robotics], [[University of Massachusetts Amherst]] Amherst, USA
* [https://web.archive.org/web/20170714151253/http://www.tech.plym.ac.uk/SoCCE/CRNS/ Centre for Robotics and Neural Systems], [http://www.plymouth.ac.uk/ Plymouth University] Plymouth, United Kingdom
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