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2nd IEEE Colombian Conference on Computational Intelligence (ColCACI)

作者： Jaramillo-Henao, Liz A. Velez-Aristizabal, Adrian A. Arango-Castro, Jaime E. Univ Nacl Colombia Dept Elect & Elect Engn Manizales Caldas Colombia

ISBN: (纸本)9783030362119;9783030362102

Self-locomotion experience of infants has been argued to improve perception of distance, as visual attention is drawn to previously undetected or ignored depth specifying information. We present a computational model to evaluate how does self-locomotion experience influences the estimation of distance in infants. The model assigns an estimated distance label to salient objects in the scene, through a Binocular Neural Network (BNN) that computes binocular disparities. Emphasizing on key aspects of locomotion experience, two BNN are trained, one for non-locomotor infants and one for locomotor infants. The validation and test stages of the process show a significant improvement on the distance estimation task for the BNN trained with locomotor experience. This result is added to previous evidence which supports that locomotion in infants is an important step in cognitive development.

关键词： cognitive developmental robotics Visual attention Locomotor experience Binocular Neural Network

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A Deep Neural Network for Finger Counting and Numerosity Estimation

A Deep Neural Network for Finger Counting and Numerosity Est...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Pecyna, Leszek Cangelosi, Angelo Di Nuovo, Alessandro Univ Plymouth Ctr Robot & Neural Syst Plymouth Devon England Univ Manchester Manchester Lancs England ICAR CNR Inst Manchester Lancs England Sheffield Hallam Univ Sheffield Robot Sheffield S Yorkshire England

ISBN: (纸本)9781728124858

In this paper, we present neuro-robotics models with a deep artificial neural network capable of generating finger counting positions and number estimation. We first train the model in an unsupervised manner where each layer is treated as a Restricted Boltzmann Machine or an autoencoder. Such a model is further trained in a supervised way. This type of pre-training is tested on our baseline model and two methods of pre-training are compared. The network is extended to produce finger counting positions. The performance in number estimation of such an extended model is evaluated. We test the hypothesis if the subitizing process can be obtained by one single model used also for estimation of higher numerosities. The results confirm the importance of unsupervised training in our enumeration task and show some similarities to human behaviour in the case of subitizing.

关键词： numerosity estimation counting embodied cognition gestures unsupervised training deep neural network cognitive developmental robotics subitizing

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HUMANOID PLATFORMS FOR cognitive developmental robotics

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INTERNATIONAL JOURNAL OF HUMANOID robotics 2011年第3期8卷 391-418页

作者： Ishiguro, Hiroshi Minato, Takashi Yoshikawa, Yuichiro Asada, Minoru Osaka Univ Grad Sch Engn Sci Toyonaka Osaka 5608531 Japan Japan Sci & Technol Agcy ERATO Asada Project Suita Osaka 5650871 Japan Osaka Univ Grad Sch Engn Suita Osaka 5650871 Japan

One of the most promising approaches to understand human cognitive and developmental mechanisms is a synthetic approach using humanoid robots;an approach to understand the human cognitive functions by realizing them with the robots. Humans are so complicated and it is difficult to mimic the well-developed human by robotic technologies. Therefore, it is necessary to understand how humans develop the complicated functions during the developmental process. We may be able to develop infant functions and make them evolve by tracing the human developmental process. This new study requires robot platforms that can mimic various aspects of the human developmental process. This paper introduces a series of robot platforms that we have developed for the studies with the synthetic approach.

关键词： cognitive developmental robotics platform development Human-robot interaction

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Long-Short Term Memory Networks for Modelling Embodied Mathematical Cognition in Robots

Long-Short Term Memory Networks for Modelling Embodied Mathe...

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International Joint Conference on Neural Networks (IJCNN)

作者： Di Nuovo, Alessandro Sheffield Hallam Univ Dept Comp & Sheffield Robot Sheffield S Yorkshire England

ISBN: (纸本)9781509060146

Mathematical competence can endow robots with the necessary capability for abstract and symbolic processing, which is required for higher cognitive functions such as natural language understanding. But, so far, only few attempts have been made to model mathematical cognition in robots. This paper presents an experimental evaluation of the Long-Short Term Memory networks for modeling the simple mathematical operation of single-digits addition in a cognitive robot. To this end, the robotic model creates an association between the proprioceptive information from finger counting and the handwritten digits of the MNIST dataset. In practice, the model executes two tasks concurrently: it recognizes the handwritten digits in a sequence and sums them. The results show that the association with fingers can improve the robot precision, as observed in children. Also, the robot makes a disproportionate number of split-five errors similarly to what observed in studies with children and adults, hence giving evidence to support the hypothesis that these errors are due the use of a five-fingers counting system.

关键词： Long Short-Term Memory networks Deep Learning cognitive developmental robotics Mathematical Cognition

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A Re-description Based developmental Approach to the Generation of Value Functions for cognitive Robots 13th

A Re-description Based Developmental Approach to the Generat...

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13th International Conference on Hybrid Artificial Intelligent Systems (HAIS)

作者： Romero, A. Bellas, F. Prieto, A. Duro, R. J. Univ A Coruna Integrated Grp Engn Res La Coruna Spain

ISBN: (纸本)9783319926391;9783319926384

Motivation is a fundamental topic when implementing cognitive architectures aimed at lifelong open-ended learning in autonomous robots. In particular, it is of paramount importance for these types of architectures to be able to establish goals that provide purpose to the robot's interaction with the world as well as to progressively learn value functions within its state space that allow reaching those goals whatever the starting point. This paper aims at exploring a developmental approach to the generation of high level neural network based value functions in complex continuous state spaces through a re-description process. This process starts by obtaining relatively simple Separable Utility Regions (SURs) which allow the system to consistently achieve goals, although not necessarily in the most efficient manner. The traces obtained by these SURs are then used to provide training data for a neural network based value function. Through a simple experiment with the Robobo robot, we show that this procedure can be more generalizable than attempting to directly obtain the value function through more traditional means.

关键词： cognitive developmental robotics Motivation Value function

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Motivational engine with autonomous sub-goal identification for the Multilevel Darwinist Brain

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BIOLOGICALLY INSPIRED cognitive ARCHITECTURES 2016年第0期17卷 1-11页

作者： Salgado, R. Prieto, A. Bellas, F. Calvo-Varela, L. Duro, R. J. Univ A Coruna Integrated Grp Engn Res La Coruna Spain

This work proposes a motivational system for an autonomous robot that guides the fulfillment of its goals in a developmental manner, discovering sub-goals not only as a way to simplify goal achievement, but as a way to acquire knowledge in an incremental, modular and reusable fashion. This system has been called MotivEn (Motivational Engine) and we have carried out its initial integration within the Multilevel Darwinist Brain (MDB) cognitive architecture. We describe here the main elements of MotivEn and how they improve the current MDB operation. Moreover, we present in detail a specific implementation of MotivEn and the application results obtained in terms of sub-goal identification when applying it in a real robot experiment with the MDB. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Sub-goal identification Motivation cognitive developmental robotics cognitive architecture

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Emergence of Altruistic Behavior Through the Minimization of Prediction Error

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IEEE TRANSACTIONS ON cognitive AND developmental SYSTEMS 2016年第3期8卷 141-151页

作者： Baraglia, Jimmy Nagai, Yukie Asada, Minoru Osaka Univ Dept Adapt Machine Syst Suita Osaka 5650871 Japan

The emergence of altruistic behavior in infants fosters their social development and supports their involvement in our society. Altruistic tendencies, intended to benefit others with no apparent rewards, are also very useful for social robots that are designed to be used in our households. Yet, to make robots capable of learning how to help others as infants do, it is important to understand the mechanisms and motives responsible for the development of altruistic behavior. Further, understanding the mechanisms behind the early development of pro-social behavior would be a great contribution to the field of developmental psychology. To these ends, we hypothesize that infants from 14 months of age help others to minimize the differences between predicted actions and observations, that is, to minimize prediction errors. To evaluate our hypothesis, we created a computational model based on psychological studies and implemented it in real and simulated robots. Our system first acquires its own sensory-motor representation by interacting with its environment. Then, using its experience, the system recognizes and predicts others' actions and uses this prediction to estimate a prediction error. Our experiments demonstrated that our robots could spontaneously generate helping behaviors by being motivated by the minimization of prediction errors.

关键词： Altruistic behavior cognitive developmental robotics helping behavior prediction error

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Self and Non-self Discrimination Mechanism Based on Predictive Learning with Estimation of Uncertainty 1

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23rd International Conference on Neural Information Processing (ICONIP)

作者： Nakajo, Ryoichi Takahashi, Maasa Murata, Shingo Arie, Hiroaki Ogata, Tetsuya Waseda Univ Dept Intermedia Art & Sci Tokyo Japan Waseda Univ Dept Modern Mech Engn Tokyo Japan

ISBN: (数字)9783319466811

ISBN: (纸本)9783319466811;9783319466804

In this paper, we propose a model that can explain the mechanism of self and non-self discrimination. Infants gradually develop their abilities for self-other cognition through interaction with the environment. Predictive learning has been widely used to explain the mechanism of infants' development. We hypothesized that infants' cognitive abilities are developed through predictive learning and the uncertainty estimation of their sensory-motor inputs. We chose a stochastic continuous time recurrent neural network, which is a dynamical neural network model, to predict uncertainties as variances. From the perspective of cognitive developmental robotics, a predictive learning experiment with a robot was performed. The results indicate that training made the robot predict the regions related to its body more easily. We confirmed that self and non-self cognitive abilities might be acquired through predictive learning with uncertainty estimation.

关键词： Self/non-self cognition cognitive developmental robotics Recurrent neural network

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Pragmatic Frames for Teaching and Learning in Human-Robot interaction: Review and Challenges

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FRONTIERS IN NEUROrobotics 2016年第OCT期10卷 10页

作者： Vollmer, Anna-Lisa Wrede, Britta Rohlfing, Katharina J. Oudeyer, Pierre-Yves Inria FLOWERS Team Paris France ENSTA ParisTech Paris France Univ Bielefeld CITEC Appl Informat Grp Bielefeld Germany Univ Paderborn Psycholinguist Grp Paderborn Germany

One of the big challenges in robotics today is to learn from human users that are inexperienced in interacting with robots but yet are often used to teach skills flexibly to other humans and to children in particular. A potential route toward natural and efficient learning and teaching in Human-Robot Interaction (HRI) is to leverage the social competences of humans and the underlying interactional mechanisms. In this perspective, this article discusses the importance of pragmatic frames as flexible interaction protocols that provide important contextual cues to enable learners to infer new action or language skills and teachers to convey these cues. After defining and discussing the concept of pragmatic frames, grounded in decades of research in developmental psychology, we study a selection of HRI work in the literature which has focused on learning-teaching interaction and analyze the interactional and learning mechanisms that were used in the light of pragmatic frames. This allows us to show that many of the works have already used in practice, but not always explicitly, basic elements of the pragmatic frames machinery. However, we also show that pragmatic frames have so far been used in a very restricted way as compared to how they are used in human-human interaction and argue that this has been an obstacle preventing robust natural multi-task learning and teaching in HRI. In particular, we explain that two central features of human pragmatic frames, mostly absent of existing HRI studies, are that (1) social peers use rich repertoires of frames, potentially combined together, to convey and infer multiple kinds of cues;(2) new frames can be learnt continually, building on existing ones, and guiding the interaction toward higher levels of complexity and expressivity. To conclude, we give an outlook on the future research direction describing the relevant key challenges that need to be solved for leveraging pragmatic frames for robot learning and teaching.

关键词： robot learning robot teaching human-robot interaction pragmatic frames social learning language learning action learning cognitive developmental robotics

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Preferential training of neurodynamical model based on predictability of target dynamics

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ADVANCED robotics 2015年第9期29卷 587-596页

作者： Nishide, Shun Nobuta, Harumitsu Okuno, Hiroshi G. Ogata, Tetsuya Univ Tokushima Inst Sci & Technol Tokushima 770 Japan Mixi Inc Tokyo Japan Waseda Univ Grad Sch Fundamental Sci & Engn Tokyo Japan

Intrinsic motivation is one of the keys in implementing the mechanism of interest to robots. In this paper, we present a method to apply intrinsic motivation in dynamics learning with predictable and unpredictable targets in view. The robot's arm is used for the predictable target and the human's arm is used for the unpredictable target in the experiment. The learning algorithm based on intrinsic motivation will automatically set a larger weight to targets that would contribute to decreasing the training error, while setting a smaller weight to others. A neurodynamical model, namely multiple timescales recurrent neural network (MTRNN), is utilized for studying the robot's arm/external object dynamics. Training of MTRNN is done using the back propagation through time (BPTT) algorithm. We modify the BPTT algorithm by the following two steps. (1) Evaluate predictability of robot's arm/objects using training error of MTRNN. (2) Assign a preference ratio, which represents the weight of the training, to each object based on predictability. The proposed training method would focus more on reducing training error of predictable objects compared to normal BPTT, where training error is equally treated for every object. Experiments were conducted using an actual robot platform, moving the robot's arm while a human moves his arm in the robot's camera view. The results of the experiment showed that the proposed training method could achieve smaller training error of the robot's arm visuomotor dynamics, which is predictable from the robot's motor command, compared to general training with BPTT. Evaluation of MTRNN as a forward model to predict untrained data showed that the proposed model is capable of predicting the robot's hand motion, specifically with larger number of nodes.

关键词： cognitive developmental robotics recurrent neural network intrinsic motivation

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