2011 IEEE International Conference on Development and Learning (ICDL)最新文献_第5页

Development of object manipulation through self-exploratory visuomotor experience 通过自我探索的视觉运动体验来发展物体操作

2011 IEEE International Conference on Development and Learning (ICDL) Pub Date : 2011-10-10 DOI: 10.1109/DEVLRN.2011.6037362

Kenta Kawamoto, K. Noda, Takashi Hasuo, K. Sabe

引用次数: 1

Ignorance is bliss: A complexity perspective on adapting reactive architectures 无知是福:适应响应式架构的复杂性视角

2011 IEEE International Conference on Development and Learning (ICDL) Pub Date : 2011-10-10 DOI: 10.1109/DEVLRN.2011.6037337

T. Wareham, J. Kwisthout, W. Haselager, I. Rooij

引用次数: 12

Intrinsic Activitity: from motor babbling to play 内在活动:从咿呀学语到玩耍

2011 IEEE International Conference on Development and Learning (ICDL) Pub Date : 2011-10-10 DOI: 10.1109/DEVLRN.2011.6037375

Mark H. Lee

引用次数: 21

Sequential pattern mining of multimodal data streams in dyadic interactions 二元交互中多模态数据流的顺序模式挖掘

2011 IEEE International Conference on Development and Learning (ICDL) Pub Date : 2011-10-10 DOI: 10.1109/DEVLRN.2011.6037334

Damian Fricker, Hui Zhang, Chen Yu

引用次数: 23

Bootstrapping word learning: A perception driven semantics-first approach 自引导词学习:感知驱动的语义优先方法

2011 IEEE International Conference on Development and Learning (ICDL) Pub Date : 2011-10-10 DOI: 10.1109/DEVLRN.2011.6037345

A. Mukerjee, Nikhil Joshi, Prabhat Mudgal, S. Srinath

{"title":"Bootstrapping word learning: A perception driven semantics-first approach","authors":"A. Mukerjee, Nikhil Joshi, Prabhat Mudgal, S. Srinath","doi":"10.1109/DEVLRN.2011.6037345","DOIUrl":"https://doi.org/10.1109/DEVLRN.2011.6037345","url":null,"abstract":"In recent decades, evidence for preverbal perceptual categorization in infants has been accumulating, and a role has been suggested for such processes in bootstrapping word learning, i.e. acquiring the very first word-meaning associations. We propose a computational study to consider the possibility that initial notions of semantic classes may help word learning. We consider visual scenes with many possible referents, and consider unparsed linguistic descriptions in text form. We use no prior knowledge of vision domain, or of morphology, syntax or word frequency. Using a synthetic model of object attention, we show that the system is able to first identify perceptual classes from the visual stream, and then associate these with words from the linguistic stream. Working with Hindi text, we demonstrate the ability to learn words for prominent proto-concepts like BICYCLE, TRUCK, and CAR from a complex traffic video. We compare the associations when learning unsegmented poly-syllabic strings in the language (without knowledge of word boundaries) versus segmented words, and find that the poly-syllables do nearly as well. This suggests that early acquisition of some semantic classes may also help in parsing the input stream into “words”. The model is then used on a novel video from a similar domain, to identify objects with their labels. Since we provide no knowledge to the system either for the visual or language analyses, the results are likely to hold for other visual scenes and languages.","PeriodicalId":256921,"journal":{"name":"2011 IEEE International Conference on Development and Learning (ICDL)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115665625","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: 1

Making a robotic scene representation accessible to feature and label queries 使机器人场景表示可访问的特征和标签查询

2011 IEEE International Conference on Development and Learning (ICDL) Pub Date : 2011-10-10 DOI: 10.1109/DEVLRN.2011.6037360

Stephan K. U. Zibner, C. Faubel, G. Schöner

引用次数: 10

Ego-centric and allo-centric abstraction in self-organized hierarchical neural networks 自组织层次神经网络中的自我中心和异中心抽象

2011 IEEE International Conference on Development and Learning (ICDL) Pub Date : 2011-10-10 DOI: 10.1109/DEVLRN.2011.6037347

M. Maniadakis, J. Tani, P. Trahanias

{"title":"Ego-centric and allo-centric abstraction in self-organized hierarchical neural networks","authors":"M. Maniadakis, J. Tani, P. Trahanias","doi":"10.1109/DEVLRN.2011.6037347","DOIUrl":"https://doi.org/10.1109/DEVLRN.2011.6037347","url":null,"abstract":"The computational systems supporting the cognitive capacity of artificial agents are often structured hierarchically, with sensory-motor details placed in the lower levels, and abstracted conceptual items in the upper levels. Such an architecture mimics the structural properties of the animal and human nervous system. To operate efficiently in varying circumstances, artificial agents are necessary to consider both ego-centric (i.e. self-centered) and allo-centric (i.e. other-centered) information, which are further combined to address given tasks. The present work investigates effective assemblies for simultaneously placing ego-centric and allo-centric processes in the cognitive hierarchy, by evolving self-organized neural network controllers. The systematic study of the internal network mechanisms has showed that effective neural assemblies are developed by placing allo-centric information in the upper levels of the cognitive hierarchy, followed by ego-centric abstracted representations in the middle and finally sensory-motor details in the lower level. We present and discuss the obtained results considering how they are related with known assumptions about human brain functionality.","PeriodicalId":256921,"journal":{"name":"2011 IEEE International Conference on Development and Learning (ICDL)","volume":"171 2-3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116637174","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: 1

Familiarity-to-novelty shift driven by learning: A conceptual and computational model 由学习驱动的从熟悉到新奇的转变:一个概念和计算模型

2011 IEEE International Conference on Development and Learning (ICDL) Pub Date : 2011-10-10 DOI: 10.1109/DEVLRN.2011.6037314

Quan Wang, Pramod Chandrashekhariah, Gabriele Spina

引用次数: 5

What a successful grasp tells about the success chances of grasps in its vicinity 一次成功的抓握表明了它附近的抓握成功的机会

2011 IEEE International Conference on Development and Learning (ICDL) Pub Date : 2011-10-10 DOI: 10.1109/DEVLRN.2011.6037342

L. Bodenhagen, R. Detry, J. Piater, N. Krüger

{"title":"What a successful grasp tells about the success chances of grasps in its vicinity","authors":"L. Bodenhagen, R. Detry, J. Piater, N. Krüger","doi":"10.1109/DEVLRN.2011.6037342","DOIUrl":"https://doi.org/10.1109/DEVLRN.2011.6037342","url":null,"abstract":"Infants gradually improve their grasping competences, both in terms of motor abilities as well as in terms of the internal shape grasp representations. Grasp densities [3] provide a statistical model of such an internal learning process. In the concept of grasp densities, kernel density estimation is used based on a six-dimensional kernel representing grasps with given position and orientation. For this so far an isotropic kernel has been used which exact shape have only been weakly justified. Instead in this paper, we use an anisotropic kernel that is statistically based on measured conditional probabilities representing grasp success in the neighborhood of a successful grasp. The anisotropy has been determined utilizing a simulation environment that allowed for evaluation of large scale experiments. The anisotropic kernel has been fitted to the conditional probabilities obtained from the experiments. We then show that convergence is an important problem associated with the grasp density approach and we propose a measure for the convergence of the densities. In this context, we show that the use of the statistically grounded anisotropic kernels leads to a significantly faster convergence of grasp densities.","PeriodicalId":256921,"journal":{"name":"2011 IEEE International Conference on Development and Learning (ICDL)","volume":"143 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124589572","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: 3

Robust central pattern generators for embodied hierarchical reinforcement learning 嵌入层次强化学习的鲁棒中心模式生成器

2011 IEEE International Conference on Development and Learning (ICDL) Pub Date : 2011-10-10 DOI: 10.1109/DEVLRN.2011.6037352

M. Snel, Shimon Whiteson, Y. Kuniyoshi

{"title":"Robust central pattern generators for embodied hierarchical reinforcement learning","authors":"M. Snel, Shimon Whiteson, Y. Kuniyoshi","doi":"10.1109/DEVLRN.2011.6037352","DOIUrl":"https://doi.org/10.1109/DEVLRN.2011.6037352","url":null,"abstract":"Hierarchical organization of behavior and learning is widespread in animals and robots, among others to facilitate dealing with multiple tasks. In hierarchical reinforcement learning, agents usually have to learn to recombine or modulate low-level behaviors when facing a new task, which costs time that could potentially be saved by employing intrinsically adaptive low-level controllers. At the same time, although there exists extensive research on the use of pattern generators as low-level controllers for robot motion, the effect of their potential adaptivity on high-level performance on multiple tasks has not been explicitly studied. This paper investigates this effect using a dynamically simulated hexapod robot that needs to complete a high-level learning task on terrains of varying complexity. Results show that as terrain difficulty increases and adaptivity to environmental disturbances becomes more important, low-level controllers with a degree of instability have a positive impact on high-level performance. In particular, these controllers provide an initial performance boost that is maintained throughout learning, showing that their instability does not negatively affect their predictability, which is important for learning.","PeriodicalId":256921,"journal":{"name":"2011 IEEE International Conference on Development and Learning (ICDL)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127646332","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