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Line 1: {{more footnotes\|date=February 2012}} '''Cognitive robotics ''' or '''cognitive technology''' is a subfield of [[robotics]] concerned with endowing a robot with intelligent behavior by providing it with a processing architecture that will allow it to [[Robot learning\|learn]] and reason about how to behave in response to complex goals in a complex world. Cognitive robotics may be considered the engineering branch of [[embodied cognitive science]] and [[embodied embedded cognition]], consisting of [[robotic process automation]], [[artificial intelligence]], [[machine learning]], [[deep learning]], [[optical character recognition]], [[image processing]], [[process mining]], [[analytics]], [[software development]] and [[system integration]]. ==Core issues== While traditional [[cognitive ~~modeling~~model]]ing approaches have assumed symbolic coding schemes as a means for depicting the world, translating the world into these kinds of symbolic representations has proven to be problematic if not untenable. [[~~philosophy of perception\|~~Perception]] and [[motor cognition\|action]] and the notion of [[Mental representation\|symbolic representation]] are therefore core issues to be addressed in cognitive robotics. ==Starting point== Cognitive robotics views human or animal cognition as a starting point for the development of robotic information processing, as opposed to more traditional [[~~Artificial~~artificial ~~Intelligence~~intelligence]] techniques. Target robotic cognitive capabilities include perception processing, attention allocation, [[anticipation (artificial intelligence)\|anticipation]], planning, complex motor coordination, reasoning about other agents and perhaps even about their own mental states. Robotic cognition embodies the behavior of [[intelligent agent]]s in the physical world (or a virtual world, in the case of simulated cognitive robotics). Ultimately, the robot must be able to act in the real world. ==Learning techniques== Line 16: {{main\|Motor babbling}} A preliminary robot learning technique called [[motor babbling]] involves correlating pseudo-random complex motor movements by the robot with resulting visual and/or auditory feedback such that the robot may begin to ''expect'' a pattern of sensory feedback given a pattern of motor output. Desired sensory feedback may then be used to inform a motor control signal. This is thought to be analogous to how a baby learns to reach for objects or learns to produce speech sounds. For simpler robot systems, where, for instance, [[inverse kinematics]] may feasibly be used to transform anticipated feedback (desired motor result) into motor output, this step may be skipped. ===Imitation=== Line 24: ===Knowledge acquisition=== A more complex learning approach is "autonomous [[knowledge acquisition]]": the robot is left to explore the environment on its own. A system of goals and beliefs is typically assumed. A somewhat more directed mode of exploration can be achieved by "curiosity" algorithms, such as Intelligent Adaptive Curiosity<ref>http://www.pyoudeyer.com/ims.pdf {{Bare URL PDF\|date=March 2022}}</ref><ref>http://www.pyoudeyer.com/oudeyer-kaplan-neurorobotics.pdf {{Bare URL PDF\|date=March 2022}}</ref> or Category-Based Intrinsic Motivation.<ref>http://science.slc.edu/~jmarshall/papers/cbim-epirob09.pdf {{Bare URL PDF\|date=March 2022}}</ref> These algorithms generally involve breaking sensory input into a finite number of categories and assigning some sort of prediction system (such as an [[~~Artificial~~artificial ~~Neural~~neural ~~Network~~network]]) to each. The prediction system keeps track of the error in its predictions over time. Reduction in prediction error is considered learning. The robot then preferentially explores categories in which it is learning (or reducing prediction error) the fastest. ==Other architectures== Some researchers in cognitive robotics have tried using architectures such as ([[ACT-R]] and [[Soar (cognitive architecture)]]) as a basis of their cognitive robotics programs. These highly modular symbol-processing architectures have been used to simulate operator performance and human performance when modeling simplistic and symbolized laboratory data. The idea is to extend these architectures to handle real-world sensory input as that input continuously unfolds through time. What is needed is a way to somehow translate the world into a set of symbols and their relationships. ~~==icb metric systematic manifold==~~ drones or subatomic ai are some of the most controversial and greatest representation of sentient robotics and their processing or what the common people refer and believe in as quantum computing or plutonic A.I. (not to be confused with platonic A.I.) use string theory or mass sentient (sentient, mass) relativity to conduct a universal occlusion called schripp or string theory using schripp for the general process and purpose of creating A.I. or icb metric drones uses quantum relativity between two units to synchronize and assimilate a wavelength to cause universal fractal hyper conclusion and create an icb3 geometric manifold and conclude computing singularity units hypothetically for units to quantum compute icb3 metric numbers and code computers use a conjunction of high pitched AM frequency to randomize photogenic relay combined with a high axis accelerometer with the implementation and use of quantum source code and with inductive relay for reasoning skills that are very close to the geometric decision based system that the human race used for evolution ~~inclusively space time to manifold and icb metric singularity are generally heard in a frequency that is beyond the range of human hearing~~ ==Questions== Some of the fundamental questions to ~~still~~ be answered in cognitive robotics are:▼ ▲Some of the fundamental questions to still be answered in cognitive robotics are: * How much human programming should or can be involved to support the learning processes? * How can one quantify progress? Some of the adopted ways ~~is the~~are reward and punishment. But what kind of reward and what kind of punishment? In humans, when teaching a child, for example, the reward would be candy or some encouragement, and the punishment can take many forms. But what is an effective way with robots?{{Citation needed\|date=August 2019\|reason=It seems that this passage contains confusion about reinforcement learning, a citation to a source that shows how to use 'reward and punishment' for 'progress quantification' is needed.}} == Books ==▼ Cognitive Robotics book <ref>{{Cite web\|title = Cognitive Robotics\|url = https://www.crcpress.com/Cognitive-Robotics/Samani/9781482244564\|website = CRC Press\|accessdate = 2015-10-07}}</ref> by Hooman Samani,<ref>{{Cite web\|title = Hooman Samani\|url = http://www.hoomansamani.com/\|website = www.hoomansamani.com\|accessdate = 2015-10-07}}</ref> takes a multidiciplinary approach to cover various aspects of cognitive robotics such as artificial intelligence, physical, chemical, philosophical, psychological, social, cultural, and ethical aspects. ==See also== [[Artificial intelligence]] [[Intelligent agent]] [[Cognitive architecture]] [[Cognitive science]] [[Cybernetics]] Line 57 ⟶ 48: [[Evolutionary robotics]] [[Hybrid intelligent system]] [[iCub]]▼ [[Intelligent control]] == References == {{Reflist}} ▲== ~~Books~~Sources == [https://web.archive.org/web/20090314232056/http://www.ss-rics.org/ The Symbolic and Subsymbolic Robotic Intelligence Control System (SS-RICS)] [https://web.archive.org/web/20060617202124/http://www.cs.uu.nl/groups/IS/robotics/robotics.html Intelligent Systems Group - University of Utrecht] Line 66 ⟶ 60: The [[IDSIA]] [http://robotics.idsia.ch/ Robotics Lab] and [http://www.idsia.ch/~juergen/cogbotlab.html Cognitive Robotics Lab] of [[Juergen Schmidhuber]] [https://web.archive.org/web/20060218074249/http://www.inl.gov/adaptiverobotics/humanoidrobotics/future.shtml What Does the Future Hold for Cognitive Robots? - Idaho National Laboratory] [http://www.nrl.navy.mil/aic/iss/aas/CognitiveRobots.php Cognitive Robotics at the Naval Research Laboratory] {{Webarchive\|url=https://web.archive.org/web/20100808015544/http://www.nrl.navy.mil/aic/iss/aas/CognitiveRobots.php \|date=2010-08-08 }} [http://cogrob.ensta.fr/ Cognitive robotics at ENSTA] autonomous embodied systems, evolving in complex and non-constraint environments, using mainly vision as sensor. [https://web.archive.org/web/20070504203412/http://eecs.vanderbilt.edu/CIS/cisHome.shtml The Center for Intelligent Systems - Vanderbilt University] [http://www.cor-lab.de/ Institute for Cognition and Robotics (CoR-Lab) at Bielefeld University] [https://archive.today/20130222165616/http://mmi.tudelft.nl/SocioCognitiveRobotics/ SocioCognitive Robotics at Delft University of Technology] [https://web.archive.org/web/20130521072036/http://www.aslab.upm.es/ Autonomous Systems Laboratory at Universidad Politecnica de Madrid] [https://www.inf.uni-hamburg.de/en/inst/ab/wtm/ Knowledge Technology at Universität Hamburg] [http://www.ida.liu.se/conferences/cogrob2014/ The Cognitive Robotics Association], founded in 1998, directed by Gerhard Lakemeyer, University of Aachen, organizes every two ~~year~~years the Cognitive Robotics Workshop and it is generously supported by [http://www.journals.elsevier.com/artificial-intelligence/ the AI journal] == External links == ▲ [[iCub]] * [https://web.archive.org/web/20070619021836/http://mensnewsdaily.com/2007/05/16/robobusiness-robots-with-imagination/ RoboBusiness: Robots that Dream of Being Better] * [https://web.archive.org/web/20071008234511/http://www.conscious-robots.com/en/conscious-machines/the-field-of-machine-consciousness/cognitive-rob.html www.Conscious-Robots.com] * [https://web.archive.org/web/20160304222937/http://mailman.rwth-aachen.de/mailman/listinfo/cogrob-sc The ~~cognitive~~Cognitive Robotics Association] {{Robotics}} [[Category:~~Artificial~~Branches ~~intelligence~~of cognitive science]] [[Category:Robotics]]▼ [[Category:Machine learning]] ▲[[Category:Robotics]]