2015 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob)最新文献_第4页

Effect of exploratory perturbation on the formation of kinematic synergies in Goal Babbling 探索性扰动对目标咿呀学语运动协同形成的影响

2015 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) Pub Date : 2015-12-07 DOI: 10.1109/DEVLRN.2015.7346120

Kenichi Narioka, R. F. Reinhart, Jochen J. Steil

引用次数: 5

Bootstrapping interactions with objects from raw sensorimotor data: A novelty search based approach 从原始感觉运动数据中引导与对象的交互:基于新颖性搜索的方法

2015 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) Pub Date : 2015-12-07 DOI: 10.1109/DEVLRN.2015.7346098

Carlos Maestre, Antoine Cully, Christophe Gonzales, S. Doncieux

{"title":"Bootstrapping interactions with objects from raw sensorimotor data: A novelty search based approach","authors":"Carlos Maestre, Antoine Cully, Christophe Gonzales, S. Doncieux","doi":"10.1109/DEVLRN.2015.7346098","DOIUrl":"https://doi.org/10.1109/DEVLRN.2015.7346098","url":null,"abstract":"Determining in advance all objects that a robot will interact with in an open environment is very challenging, if not impossible. It makes difficult the development of models that will allow to perceive and recognize objects, to interact with them and to predict how these objects will react to interactions with other objects or with the robot. Developmental robotics proposes to make robots learn by themselves such models through a dedicated exploration step. It raises a chicken-and-egg problem: the robot needs to learn about objects to discover how to interact with them and, to this end, it needs to interact with them. In this work, we propose Novelty-driven Evolutionary Babbling (NovEB), an approach enabling to bootstrap this process and to acquire knowledge about objects in the surrounding environment without requiring to include a priori knowledge about the environment, including objects, or about the means to interact with them. Our approach consists in using an evolutionary algorithm driven by a novelty criterion defined in the raw sensorimotor flow: behaviours, described by a trajectory of the robot end effector, are generated with the goal to maximize the novelty of raw perceptions. The approach is tested on a simulated PR2 robot and is compared to a random motor babbling.","PeriodicalId":164756,"journal":{"name":"2015 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116813509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 12

The SB-ST decomposition in the study of Developmental Coordination Disorder 发育协调障碍研究中的SB-ST分解

2015 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) Pub Date : 2015-12-07 DOI: 10.1109/DEVLRN.2015.7346131

L. Claudino, Jane E. Clark, Y. Aloimonos

{"title":"The SB-ST decomposition in the study of Developmental Coordination Disorder","authors":"L. Claudino, Jane E. Clark, Y. Aloimonos","doi":"10.1109/DEVLRN.2015.7346131","DOIUrl":"https://doi.org/10.1109/DEVLRN.2015.7346131","url":null,"abstract":"To deal with redundancy and high-dimensionality that are typical of movement data, we propose to decompose action matrices in two decoupled steps: first, we discover a set of key postures, that is, vectors corresponding to key relationships among degrees of freedom (like angles between body parts) which we call spatial basis (SB) and second, we impose a parametric model to the spatio-temporal (ST) profiles of each SB vector. These two steps constitute the SB-ST decomposition of an action: SB vectors represent the key postures, their ST profiles represent trajectories of these postures and ST parameters express how these postures are being controlled and coordinated. SB-ST shares elements in common with computational models of motor synergies and biological motion perception, and it relates to human manifold models that are popular in machine learning. We showcase the method by applying SB vectors and ST parameters to study vertical jumps of adults, typically developing children and children with Developmental Coordination Disorder obtained with motion capture. Using that data, we also evaluate SB-ST alone and against other techniques in terms of reconstruction ability and number of dimensions used.","PeriodicalId":164756,"journal":{"name":"2015 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115979015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Grounding object perception in a naive agent's sensorimotor experience 幼稚主体感觉运动经验中客体知觉的基础

2015 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) Pub Date : 2015-12-07 DOI: 10.1109/DEVLRN.2015.7346156

Alban Laflaquière, Nikolas J. Hemion

引用次数: 8

Adaptive joint trajectory generator based on a chaotic recurrent neural network 基于混沌递归神经网络的自适应联合轨迹发生器

2015 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) Pub Date : 2015-12-02 DOI: 10.1109/DEVLRN.2015.7346158

M. Folgheraiter, Nazgul Tazhigaliyeva, Aibek S. Niyetkaliyev

引用次数: 0

Acquisition of viewpoint representation in imitative learning from own sensory-motor experiences 从自己的感觉-运动经验中习得模仿学习中的观点表征

2015 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) Pub Date : 2015-12-02 DOI: 10.1109/DEVLRN.2015.7346166

Ryoichi Nakajo, Shingo Murata, H. Arie, T. Ogata

{"title":"Acquisition of viewpoint representation in imitative learning from own sensory-motor experiences","authors":"Ryoichi Nakajo, Shingo Murata, H. Arie, T. Ogata","doi":"10.1109/DEVLRN.2015.7346166","DOIUrl":"https://doi.org/10.1109/DEVLRN.2015.7346166","url":null,"abstract":"This paper introduces an imitative model that enables a robot to acquire viewpoints of the self and others from its own sensory-motor experiences. This is important for recognizing and imitating actions generated from various directions. Existing methods require coordinate transformations input by human designers or complex learning modules to acquire a viewpoint. In the proposed model, several neurons dedicated to generated actions and viewpoints of the self and others are added to a dynamic nueral network model reffered as continuous time recurrent neural network (CTRNN). The training data are labeled with types of actions and viewpoints, and are linked to each internal state. We implemented this model in a robot and trained the model to perform actions of object manipulation. Representations of behavior and viewpoint were formed in the internal states of the CTRNN. In addition, we analyzed the initial values of the internal states that represent the viewpoint information. We confirmed the distinction of the observational perspective of other's actions self-organized in the space of the initial values. Combining the initial values of the internal states that describe the behavior and the viewpoint, the system can generate unlearned data.","PeriodicalId":164756,"journal":{"name":"2015 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123326993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Predictive learning with uncertainty estimation for modeling infants' cognitive development with caregivers: A neurorobotics experiment 基于不确定性估计的预测学习在照顾者对婴儿认知发展建模中的应用:一项神经机器人实验

2015 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) Pub Date : 2015-12-02 DOI: 10.1109/DEVLRN.2015.7346162

Shingo Murata, Saki Tomioka, Ryoichi Nakajo, Tatsuro Yamada, H. Arie, T. Ogata, S. Sugano

{"title":"Predictive learning with uncertainty estimation for modeling infants' cognitive development with caregivers: A neurorobotics experiment","authors":"Shingo Murata, Saki Tomioka, Ryoichi Nakajo, Tatsuro Yamada, H. Arie, T. Ogata, S. Sugano","doi":"10.1109/DEVLRN.2015.7346162","DOIUrl":"https://doi.org/10.1109/DEVLRN.2015.7346162","url":null,"abstract":"Dynamic interactions with caregivers are essential for infants to develop cognitive abilities, including aspects of action, perception, and attention. We hypothesized that these abilities can be acquired through the predictive learning of sensory inputs including their uncertainty (inverse precision) in terms of variance. To examine our hypothesis from the perspective of cognitive developmental robotics, we conducted a neurorobotics experiment involving a ball-playing interaction task between a human experimenter representing a caregiver and a small humanoid robot representing an infant. The robot was equipped with a dynamic generative model called a stochastic continuous-time recurrent neural network (S-CTRNN). The S-CTRNN learned to generate predictions about both the visuo-proprioceptive states of the robot and the uncertainty of these states by minimizing a negative log-likelihood consisting of log-uncertainty and precision-weighted prediction error. The experimental results showed that predictive learning with uncertainty estimation enabled the robot to acquire infant-like cognitive abilities through dynamic interactions with the experimenter. We also discuss the effects of infant-directed modifications observed in caregiver-infant interactions on the development of these abilities.","PeriodicalId":164756,"journal":{"name":"2015 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124861302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Exploration strategies for incremental learning of object-based visual saliency 基于对象的视觉显著性增量学习的探索策略

2015 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) Pub Date : 2015-08-13 DOI: 10.1109/DEVLRN.2015.7346099

Céline Craye, David Filliat, Jean-François Goudou

引用次数: 19

Active learning of local predictable representations with artificial curiosity 基于人工好奇心的局部可预测表征的主动学习

2015 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) Pub Date : 2015-08-13 DOI: 10.1109/devlrn.2015.7346145

Mathieu Lefort, A. Gepperth

{"title":"Active learning of local predictable representations with artificial curiosity","authors":"Mathieu Lefort, A. Gepperth","doi":"10.1109/devlrn.2015.7346145","DOIUrl":"https://doi.org/10.1109/devlrn.2015.7346145","url":null,"abstract":"In this article, we present some preliminary work on integrating an artificial curiosity mechanism in PROPRE, a generic and modular neural architecture, to obtain online, open-ended and active learning of a sensory-motor space, where large areas can be unlearnable. PROPRE consists of the combination of the projection of the input motor flow, using a self-organizing map, with the regression of the sensory output flow from this projection representation, using a linear regression. The main feature of PROPRE is the use of a predictability module that provides an interestingness measure for the current motor stimulus depending on a simple evaluation of the sensory prediction quality. This measure modulates the projection learning so that to favor the representations that predict the output better than a local average. Especially, this leads to the learning of local representations where an input/output relationship is defined [1]. In this article, we propose an artificial curiosity mechanism based on the monitoring of learning progress, as proposed in [2], in the neighborhood of each local representation. Thus, PROPRE simultaneously learns interesting representations of the input flow (depending on their capacities to predict the output) and explores actively this input space where the learning progress is the higher. We illustrate our architecture on the learning of a direct model of an arm whose hand can only be perceived in a restricted visual space. The modulation of the projection learning leads to a better performance and the use of the curiosity mechanism provides quicker learning and even improves the final performance.","PeriodicalId":164756,"journal":{"name":"2015 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob)","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134339290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Representations of body schemas for infant robot development 婴儿机器人发育的身体图式表征

2015 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) Pub Date : 2015-08-13 DOI: 10.1109/DEVLRN.2015.7346128

Patricia Shaw, J. Law, Mark H. Lee

引用次数: 2