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5th IEEE Joint International Conference on Development and Learning and on Epigenetic robotics (IEEE ICDL-EpiRob)

作者： Di Nuovo, Alessandro De La Cruz, Vivian M. Cangelosi, Angelo Univ Plymouth Ctr Robot & Neural Syst Plymouth PL4 8AA Devon England Kore Univ Fac Engn & Architecture Seoul Italy Univ Catania Dept Cognit Proc Catania CT Italy

ISBN: (纸本)9781467393201

The novel deep learning paradigm offers a highly biologically plausible way to train neural network architectures with many layers, inspired by the hierarchical organization of the human brain. Indeed, deep learning gives a new dimension to research modeling human cognitive behaviors, and provides new opportunities for applications in cognitive robotics. In this paper, we present a novel deep neural network architecture for number cognition by means of finger counting and number words. The architecture is composed of 5 layers and is designed in a way that allows it to learn numbers from one to ten by associating the sensory inputs (motor and auditory) coming from the iCub humanoid robotic platform. The architecture performance is validated and tested in two developmental experiments. In the first experiment, standard backpropagation is compared with a deep learning approach, in which weights and biases are pre-trained by means of a greedy algorithm and then refined with backpropagation. In the second experiment, six bi-cultural number learning conditions are compared to explore the impact of different languages (for number words) and finger counting strategies. The developmental experiments confirm the validity of the model and the increase in efficiency given by the deep learning approach. Results of the bi-cultural study are presented and discussed with respect to the neuro-psychological literature and implications of the results for learning situations are briefly outlined.

关键词： Deep Learning Number Cognition cognitive developmental robotics Embodiment Bi-Cultural learning

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Development of goal-directed gaze shift based on predictive learning 4

Development of goal-directed gaze shift based on predictive ...

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4th Joint IEEE International Conferences on Development and Learning and Epigenetic robotics (IEEE ICDL-Epirob)

作者： Copete, Jorge Luis Nagai, Yukie Asada, Minoru Osaka Univ Dept Adapt Machine Syst Grad Sch Engn Osaka Japan

ISBN: (纸本)9781479975402

Understanding others' actions as goal-directed is a key mechanism to develop social cognitive abilities such as imitation and cooperation. Recent findings in psychology have demonstrated that the ability to predict the goal of observed actions emerges as early as six months in infancy. However, what mechanisms are involved and how they trigger the development of this ability are still open questions. In this paper, we propose a computational model employing a recurrent neural network to reproduce the developmental process of goal-directed gaze shift. Our hypothesis is that it is possible to infer the goal of observed actions by learning to predict the attention target originated from bottom-up visual attention. While keeping consistency with psychological findings, our experimental results confirmed the hypothesis that learning to predict the attention targets leads to the development of predictive gaze shift to the action goal.

关键词： cognitive developmental robotics recurrent neural network visual attention

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Grounding Fingers, Words and Numbers in a cognitive developmental Robot

Grounding Fingers, Words and Numbers in a Cognitive Developm...

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IEEE Symposium on Computational Intelligence, cognitive Algorithms, Mind, and Brain

作者： Di Nuovo, Alessandro De La Cruz, Vivian M. Cangelosi, Angelo Univ Plymouth Ctr Robot & Neural Syst Plymouth PL4 8AA Devon England Kore Univ Fac Engn & Architecture Enna En Italy Univ Messina I-98100 Messina Italy

ISBN: (纸本)9781479945498

The young math learner must make the transition from a concrete number situation, such as that of counting objects (fingers often being the most readily available), to that of using a written symbolic form that stands for the quantities the sets of objects come to represent. This challenging process is often coupled to that of learning a verbal number system that is not always transparent to children. A number of theoretical approaches have been advanced to explain aspects of how this transition takes place in cognitive development. The results obtained with the model presented here, show that a symbol grounding approach can be used to implement aspects of this transition in a cognitive robot. In the current extended version, the model develops finger and word representations, through the use of finger counting and verbal counting strategies, together with the visual representations of learned number symbols, which it uses to perform basic arithmetic operations. In the final training phases, the model is able to do this using only the number symbols as addends. We consider this an example of symbolic grounding, in that through the direct sensory experience with the body (finger counting), a category of linguistic symbol is learned (number words), and both types of representations subsequently serve to ground higher level (numerical) symbols, which are later used exclusively to perform the arithmetic operations.

关键词： symbol grounding cognitive developmental robotics number learning

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developmental Dynamics of RNNPB: New Insight about Infant Action Development

Developmental Dynamics of RNNPB: New Insight about Infant Ac...

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13th International Conference on the Simulation of Adaptive Behavior (SAB)

作者： Park, Jun-Cheol Kim, Dae-Shik Nagai, Yukie Korea Adv Inst Sci & Technol Dept Elect Engn Taejon 305701 South Korea Osaka Univ Dept Adapt Machine Syst Suita Osaka 5650871 Japan

ISBN: (纸本)9783319088648;9783319088631

developmental studies have suggested that infants' action is goal-directed. When imitating an action, younger infants tend to reproduce the goal while ignoring the means (i.e., the movement to achieve the goal) whereas older infants can imitate both. We suggest that the developmental dynamics of a Recurrent Neural Network with Parametric Bias (RNNPB) may explain the mechanism of infant development. Our RNNPB model was trained to reproduce six types of actions (2 different goals x 3 different means), during which parametric biases were self-organized to represent the difference with respect to both the goal and means. Our analysis of the self-organizing process of the parametric biases revealed an infant-like developmental change in action learning: the RNNPB first adapted to the goal and then to the means. The different saliency of these two features caused this phased development. We discuss the analogy of our result to infant action development.

关键词： cognitive developmental robotics Infant action development Recurrent neural network RNNPB

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Applying Intrinsic Motivation for Visuomotor Learning of Robot Arm Motion 11

Applying Intrinsic Motivation for Visuomotor Learning of Rob...

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11th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)

作者： Nishide, Shun Nobuta, Harumitsu Okuno, Hiroshi G. Ogata, Tetsuya Kyoto Univ Hakubi Ctr Adv Res Kyoto 6068501 Japan Mixi Inc Tokyo Japan Waseda Univ Grad Sch Fundamental Sci & Engn Tokyo 1698050 Japan

ISBN: (纸本)9781479953332

In this paper, we present a method to apply intrinsic motivation for improving visuomotor learning of robot's arm with external object in view. Multiple Timescales Recurrent Neural Network (MTRNN) is utilized for learning the robot arm/external object dynamics. Training of MTRNN is done using the Back Propagation Through Time (BPTT) algorithm. BPTT algorithm is modified as follows. 1. Evaluate predictability of robot arm/objects using training error of MTRNN. 2. Assign a preference ratio to each object based on predictability. The preference ratio represents the weight of each object to training. Experiments were conducted using an actual robot moving the arm while a human moves his arm in the robot's camera view. The result of the experiment showed that the proposed method presents better training result of robot arm visuomotor dynamics compared to general training with BPTT.

关键词： Recurrent Neural Network cognitive developmental robotics Intrinsic Motivation

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developmental Human-Robot Imitation Learning of Drawing with a Neuro Dynamical System

Developmental Human-Robot Imitation Learning of Drawing with...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Mochizuki, Keita Nishide, Shun Okuno, Hiroshi G. Ogata, Tetsuya Kyoto Univ Grad Sch Infomat Kyoto 6068501 Japan Kyoto Univ Hakubi Ctr Adv Res Kyoto 6068501 Japan Waseda Univ Sch Fundamental Sci & Engn Tokyo 1698555 Japan

ISBN: (纸本)9781479906529

This paper mainly deals with robot developmental learning on drawing and discusses the influences of physical embodiment to the task. Humans are said to develop their drawing skills through five phases: 1) Scribbling, 2) Fortuitous Realism, 3) Failed Realism, 4) Intellectual Realism, 5) Visual Realism. We implement phases 1) and 3) into the humanoid robot NAO, holding a pen, using a neuro dynamical model, namely Multiple Timescales Recurrent Neural Network (MTRNN). For phase 1), we used random arm motion of the robot as body babbling to associate motor dynamics with pen position dynamics. For phase 3), we developed incremental imitation learning to imitate and develop the robot's drawing skill using basic shapes: circle, triangle, and rectangle. We confirmed two notable features from the experiment. First, the drawing was better performed for shapes requiring arm motions used in babbling. Second, performance of clockwise drawing of circle was good from beginning, which is a similar phenomenon that can be observed in human development. The results imply the capability of the model to create a developmental robot relating to human development.

关键词： cognitive developmental robotics Imitation Learning Body Babbling Neuro Dynamical System

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developmental Human-Robot Imitation Learning of Drawing with a Neuro Dynamical System

Developmental Human-Robot Imitation Learning of Drawing with...

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Keita Mochizuki Shun Nishide Hiroshi G. Okuno Tetsuya Ogata Graduate School of Infomatics Kyoto University Kyoto Japan 606-8501 the Hakubi Center for Advanced Research Kyoto University Kyoto Japan 606-8501 School of Fundamental Science and Engineering Waseda University Tokyo Japan 169-8555

ISBN: (纸本)9781479906505

关键词： cognitive developmental robotics Imitation Learning Body Babbling Neuro Dynamical System drawing Drawing Suction human development Robot Realism Infant Vocalization role model Circles pen imitation DYNAMICAL SYSTEMS

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Automatic neural-based pattern classification of motion behaviors in autonomous robots

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NEUROCOMPUTING 2012年第1期75卷 146-155页

作者： Prieto, Abraham Bellas, Francisco Caamano, Pilar Duro, Richard J. Univ A Coruna Escuela Politecn Super Grp Integrado Ingn Ferrol 15403 A Coruna Spain

This paper addresses the problem of providing autonomous robots with a system that allows them to classify the motion behavior patterns of groups of robots present in their surroundings. It is a first step in the development of a cognitive model that can detect and understand the events occurring in the environment that are not due to the robot's own actions. The recognition of motion patterns must be achieved from the input data acquired by the robot through its camera during real time operation and, consequently, it can be addressed as a high dimensional dynamic pattern classification problem. Artificial Neural Networks (ANN) have been widely used in this type of classification problems, where a preprocessing stage is typically introduced in order to reduce dimensionality. In this stage, the processing window size and the dimensional transformation parameters must be selected according to specific domain knowledge, and they remain fixed during the ANN classification process. Such an approach is not applicable here as there is no prior information on the number of robots present or the dimensional reduction level required to describe the possible robot motion behaviors. Consequently, this work proposes a hybrid approach based on the application of a classification system called ANPAC (Automatic Neural-based Pattern Classifier) that uses a variable size ANN to perform the classification and an advisor module to adjust the preprocessing parameters and, consequently, the size of the ANN, depending on the learning results of the network. The components and operation of ANPAC are described in depth and illustrated using an example related to the recognition of behavior patterns in the motion of flocks. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Hybrid intelligent systems ANN for classification Motion pattern analysis Behavior detection cognitive developmental robotics

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Automatic Behavior Pattern Classification for Social Robots

Automatic Behavior Pattern Classification for Social Robots

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5th International Conference on Hybrid Artificial Intelligence Systems

作者： Prieto, Abraham Bellas, Francisco Caamano, Pilar Duro, Richard J. Univ A Coruna Integrated Grp Engn Res La Coruna Spain

ISBN: (纸本)9783642137686

In this paper, we focus our attention on providing robots with a system that allows them to automatically detect behavior patterns in other robots, as a first step to introducing social responsive robots. The system is called ANPAC (Automatic Neural-based Pattern Classification). Its main feature is that ANPAC automatically adjusts the optimal processing window size and obtains the appropriate features through a dimensional transformation process that allow for the classification of behavioral patterns of large groups of entities from perception datasets. Here we present the basic elements and operation of ANPAC, and illustrate its applicability through the detection of behavior patterns in the motion of flocks.

关键词： motion pattern analysis behavior detection cognitive developmental robotics social robots artificial neural networks

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Studies on humanoids

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Seimitsu Kogaku Kaishi/Journal of the Japan Society for Precision Engineering 2010年第1期76卷 20-23页

作者： Ishiguro, Hiroshi

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