Robot Learning in a Social Robot

Dominguez, Salvador; Zalama, Eduardo; Gómez-García-Bermejo, Jaime; Pulido, J.

doi:10.1007/11840541_57

Cited by 17 publications

(9 citation statements)

References 5 publications

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“…The robot is integrated into a more complex social architecture that includes robot emotion, visual tracking, user emotion recognition and a dialogue system [23] . …”

Section: Discussionmentioning

confidence: 99%

Application of the FACS in the design and construction of a mechatronic head with realistic appearance

Loza¹,

Marcos-Pablos²,

Zalama³

et al. 2013

jopha

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Abstract-The growing demand for service robots requires a better and more natural human-machine interaction. Given that an important part of human communication is non-verbal, it is necessary to endow robots with gestural communication capabilities similar to humans. This paper describes the design and construction of a realistic, mechatronic head with high gesture capacity. The proposed design is based on the human anatomy and the facial expressions are defined through the Facial Action Coding System (FACS). The paper shows the implementation details of the mechatronic head and the way a set of servomotors can generate the basic action units of FACS as well as the basic and more complex emotional gestures.

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“…The robot is integrated into a more complex social architecture that includes robot emotion, visual tracking, user emotion recognition and a dialogue system [23] . …”

Section: Discussionmentioning

confidence: 99%

Application of the FACS in the design and construction of a mechatronic head with realistic appearance

Loza¹,

Marcos-Pablos²,

Zalama³

et al. 2013

jopha

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“…In 2006, they designed learning and computing model of first and second order conditioning for a robot named Arisco with audio-visual sensation. This model was created using competition artificial neural networks and enabled Arisco to have some self-organizing functions [11]. In 1997, Gaudiano and Chang of the laboratory of Neurobotics in Boston University in the US conducted a similar research to build a neural computing model on the combination of Pavlov theory and Skinner Operant Conditioning theory in response to a navigation problem in a wheeled robot named Khepera.…”

Section: Introductionmentioning

confidence: 99%

Skinner operant conditioning model and robot bionic self-learning control

et al. 2016

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Original scientific paper A Fuzzy Skinner Operant Conditioning Automaton (FSOCA) is constructed based on Operant Conditioning Mechanism with Fuzzy Set theory. The main character of FSOCA automaton is: the fuzzed results of state by Gaussian function are used as fuzzy state sets; the fuzzy mapping rules of fuzzyconditioning-operation replace the stochastic "conditioning-operant" mapping sets. So the FSOCA automaton can be used to describe, simulate and design various self-organization actions of a fuzzy uncertain system. The FSOCA automaton firstly adopts online clustering algorithm to divide the input space and uses the excitation intensity of mapping rule to decide whether a new mapping rule needs to be generated in order to ensure that the number of mapping rules is economical. The designed FSOCA automaton is applied to motion balanced control of two-wheeled robot. With the learning proceeding, the selected probability of the optimal consequent fuzzy operant will gradually increase, the fuzzy operant action entropy will gradually decrease and the fuzzy mapping rules will automatically be generated and deleted. After about seventeen rounds of training, the selected probabilities of fuzzy consequent optimal operant gradually tend to one, the fuzzy operant action entropy gradually tends to minimum and the number of fuzzy mapping rules is optimum. So the robot gradually learns the motion balance skill. Keywords: balanced control; Fuzzy Set; mapping rules; Skinner Operant Conditioning Mechanism Model Skinner Operant Conditioning automata i bionički naučeno upravljanje robotaIzvorni znanstveni članak Fuzzy Skinner Operant Conditioning Automaton (FSOCA) sastavljen je na temelju Operant Conditioning mehanizma primjenom teorije neizrazitih skupova. Osnovno obilježje automata FSOCA je sljedeće: neizraziti rezultati stanja pomoću Gausove funkcije koriste se kao skupovi neizrazitog stanja; neizrazita pravila preslikavanja (fuzzy mapping rules) kod fuzzy-conditioning-operacije zamjenjuju stohastičke "conditioning-operant" skupove preslikavanja. Stoga se automat FSOCA može koristiti za opisivanje, simuliranje i dizajniranje raznih samo-organizirajućih radnji fuzzy nesigurnog sustava. Automat FSOCA najprije usvaja online algoritam grupiranja (clustering) u svrhu podjele ulaznog prostora (input space) te koristi intenzitet pobude pravila preslikavanja kako bi odlučio treba li generirati novo pravilo preslikavanja da bi broj pravila preslikavanja bio ekonomičan. Dizajnirani FSOCA automat primijenjen je za reguliranje balansiranja gibanja robota s dva kotača. Kako se učenje nastavlja, odabrana vjerojatnoća fuzzy operanta koji optimalno slijedi postepeno će se povećavati, entropijsko djelovanje fuzzy operanta će se postepeno smanjivati pa će se automatski generirati i izbrisati neizrazita pravila preslikavanja. Nakon otprilike sedamnaest krugova obuke, odabrane vjerojatnosti neizrazitog posljedičnog optimalnog operanta postupno teže prema jednoj, entropija djelovanja neizrazitog operanta postupno se smanjuje i broj neizrazitih p...

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“…Rosen and Goodwin et al achieved the movement balance control about an inverted pendulum within a certain distance by using Skinner's operant conditioning theory in the balance task of inverted pendulum [10]. Dominguez et al designed the competition neural network learning algorithm using Skinner's operant conditioning theory, and made the robot Arisco attain some selforganizing control skills [11]. Cai et al presented an OCPA (operant conditioning probabilistic automaton) bionic autonomous learning system based on Skinner's operant conditioning theory, and solved the balance control problem of a two-wheeled flexible robot [12].…”

Section: Introductionmentioning

confidence: 99%

A study on the cognitive model of robot sensorimotor system1

Shi

Yang

Ren

2015

IFS

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A cognitive model for sensorimotor system of self-rebalancing robot is presented based on Skinner operant conditioning principle. The model mainly consists of three parts, which are cerebellum, basal ganglia and cerebral cortex. In the model, the cerebellum realizes the mapping from sensorimotor states to actions by supervised learning mechanism, the basal ganglia decides the proper action based on the operant conditioning theory and the results of action forecast evaluation, and the cerebral cortex sends collected information to cerebellum and basal ganglia and consequently forms the closed loop feedback sensorimotor system. The structure, function and algorithm of the proposed model are presented in this paper. Simulation and experimental results on a two-wheeled robot demonstrate that this model has better cognitive characters and it enables the robot to master the skill of balance control in movement through self-learning.

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Robot Learning in a Social Robot

Cited by 17 publications

References 5 publications

Application of the FACS in the design and construction of a mechatronic head with realistic appearance

Application of the FACS in the design and construction of a mechatronic head with realistic appearance

Skinner operant conditioning model and robot bionic self-learning control

A study on the cognitive model of robot sensorimotor system1

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