2019 Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob)最新文献_第2页

Robot-Care for the Older People: Ethically Justified or Not? 机器人照顾老年人:道德上是否合理?

2019 Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) Pub Date : 2019-08-01 DOI: 10.1109/DEVLRN.2019.8850706

F. Noori, Md. Zia Uddin, J. Tørresen

引用次数: 3

KALA: Robotic Platform for Teaching Algorithmic Thinking to Children KALA:向儿童教授算法思维的机器人平台

2019 Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) Pub Date : 2019-08-01 DOI: 10.1109/DEVLRN.2019.8850694

Oscar Javier Castelblanco, Laura Milena Donado, E. Gerlein, E. González

引用次数: 1

Reaching development through visuo-proprioceptive-tactile integration on a humanoid robot - a deep learning approach 通过人形机器人的视觉-本体感觉-触觉整合实现发展——一种深度学习方法

2019 Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) Pub Date : 2019-08-01 DOI: 10.1109/DEVLRN.2019.8850681

Phuong D. H. Nguyen, M. Hoffmann, U. Pattacini, G. Metta

{"title":"Reaching development through visuo-proprioceptive-tactile integration on a humanoid robot - a deep learning approach","authors":"Phuong D. H. Nguyen, M. Hoffmann, U. Pattacini, G. Metta","doi":"10.1109/DEVLRN.2019.8850681","DOIUrl":"https://doi.org/10.1109/DEVLRN.2019.8850681","url":null,"abstract":"The development of reaching in infants has been studied for nearly nine decades. Originally, it was thought that early reaching is visually guided, but more recent evidence is suggestive of “visually elicited” reaching, i.e. infant is gazing at the object rather than its hand during the reaching movement. The importance of haptic feedback has also been emphasized. Inspired by these findings, in this work we use the simulated iCub humanoid robot to construct a model of reaching development. The robot is presented with different objects, gazes at them, and performs motor babbling with one of its arms. Successful contacts with the object are detected through tactile sensors on hand and forearm. Such events serve as the training set, constituted by images from the robot's two eyes, head joints, tactile activation, and arm joints. A deep neural network is trained with images and head joints as inputs and arm configuration and touch as output. After learning, the network can successfully infer arm configurations that would result in a successful reach, together with prediction of tactile activation (i.e. which body part would make contact). Our main contribution is twofold: (i) our pipeline is end-to-end from stereo images and head joints (6 DoF) to armtorso configurations (10 DoF) and tactile activations, without any preprocessing, explicit coordinate transformations etc.; (ii) unique to this approach, reaches with multiple effectors corresponding to different regions of the sensitive skin are possible.","PeriodicalId":318973,"journal":{"name":"2019 Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128876773","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: 8

A predictive coding model of representational drawing in human children and chimpanzees 儿童和黑猩猩具象绘画的预测编码模型

2019 Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) Pub Date : 2019-08-01 DOI: 10.1109/DEVLRN.2019.8850701

A. Philippsen, Y. Nagai

{"title":"A predictive coding model of representational drawing in human children and chimpanzees","authors":"A. Philippsen, Y. Nagai","doi":"10.1109/DEVLRN.2019.8850701","DOIUrl":"https://doi.org/10.1109/DEVLRN.2019.8850701","url":null,"abstract":"Humans and chimpanzees differ in the way that they draw. Human children from a certain age tend to create representational drawings, that is, drawings which represent objects. Chimpanzees, although equipped with sufficient motor skills, do not improve beyond the stage of scribbling behavior. To investigate the underlying cognitive mechanisms, we propose a computational model of predictive coding which allows us to change the way that sensory information and prior predictions are updated into posterior beliefs during time series prediction. We replicate the results of a study from experimental psychology which examined the ability of children and chimpanzees to complete partial drawings of a face. Our results reveal that typical or stronger reliance on the prior enables the network to perform representational drawings as observed in children. In contrast, too weak reliance on the prior replicates the findings that were observed in chimpanzees: existing lines are traced with high accuracy, but non-existing parts are not added to complete a representational drawing. The ability to perform representational drawings, thus, could be explained by subtle changes in how strongly prior information is integrated with sensory percepts rather than by the presence or absence of a specific cognitive mechanism.","PeriodicalId":318973,"journal":{"name":"2019 Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121318880","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: 11

Robots are Good for Profit: A Business Perspective on Robots in Education 机器人有利于盈利:教育领域机器人的商业视角

2019 Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) Pub Date : 2019-08-01 DOI: 10.1109/DEVLRN.2019.8850726

Michael Goudzwaard, Matthijs H. J. Smakman, E. Konijn

引用次数: 3

Efficient Intrinsically Motivated Robotic Grasping with Learning-Adaptive Imagination in Latent Space 基于潜在空间学习自适应想象的高效内在动机机器人抓取

2019 Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) Pub Date : 2019-08-01 DOI: 10.1109/DEVLRN.2019.8850723

Muhammad Burhan Hafez, C. Weber, Matthias Kerzel, S. Wermter

引用次数: 7

Replication of Infant Behaviours with a Babybot: Early Pointing Gesture Comprehension 婴儿行为与婴儿机器人的复制:早期指向手势理解

2019 Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) Pub Date : 2019-08-01 DOI: 10.1109/DEVLRN.2019.8850680

Baris Serhan, A. Cangelosi

{"title":"Replication of Infant Behaviours with a Babybot: Early Pointing Gesture Comprehension","authors":"Baris Serhan, A. Cangelosi","doi":"10.1109/DEVLRN.2019.8850680","DOIUrl":"https://doi.org/10.1109/DEVLRN.2019.8850680","url":null,"abstract":"Natural deictic communication with humanoid robots requires a mechanism for understanding pointing gestures. This mechanism should have a representation for space and time dynamics to accurately model joint covert attention. Here, we introduce a babybot that actualise a hybrid computational architecture for spatial covert attention which is embodied in the iCub humanoid robot. This developmental robotics architecture was an extension of our previous model that combines a connectionist model of pointing comprehension and a dynamic neural field model of infant saccade generation. In order to test the babybot's abilities, an attentional cueing design was built as it is a common methodology to study pointing gesture comprehension in the current developmental psychology literature. The babybot was evaluated by modelling two different age groups (i.e. 5- and 7-month-old infants) in two different attentional cueing experiments from Rohlfing et al.'s study where they have shown that a dynamic pointing covertly orients attention in early infancy as opposed to static pointing. These experiments were replicated by our babybot for all modelled age groups. The resemblance between infant and babybot behaviours supported the idea that motion information is important to disengage from the centrally salient stimulus to orient the attention to the distal ones. The current experimental setup and the model's ability to simulate different age groups provide a new platform to replicate other developmental studies on pointing comprehension to spotlight the reasons of the discrepancies between them.","PeriodicalId":318973,"journal":{"name":"2019 Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126472331","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

Autonomously learning beliefs is facilitated by a neural dynamic network driving an intentional agent 自主学习信念是由驱动意向主体的神经动态网络促进的

2019 Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) Pub Date : 2019-08-01 DOI: 10.1109/DEVLRN.2019.8850684

Jan Tekülve, G. Schöner

{"title":"Autonomously learning beliefs is facilitated by a neural dynamic network driving an intentional agent","authors":"Jan Tekülve, G. Schöner","doi":"10.1109/DEVLRN.2019.8850684","DOIUrl":"https://doi.org/10.1109/DEVLRN.2019.8850684","url":null,"abstract":"Intentionality is the capacity of mental states to be about the world, both in its “action” (world-to-mind) and its “perception” (mind-to-world) direction of fit. An intentional agent must be able to perceive, act, memorize, and plan. These psychological modes may be driven by desires and be informed by beliefs. We have previously proposed a neural process account of intentionality, in which intentional states are stabilized by interactions within populations of neurons that represent perceptual features and movement parameters. Instabilities in such neural dynamics activated the conditions of satisfaction of intentional states and induced sequences of intentional behavior. Here we explore the idea that the process organization of such intentional neural systems enables autonomous learning. We show how beliefs may be learned from single experiences, may be activated in new situations, and be used to guide behavior. Beliefs may also be dis-activated when their predictions do not match experience, leading to the learning of a new belief. We demonstrate the idea in a simple scenario in which a simulated agent autonomously explores an environment, directs action at objects and learns simple contingencies in this environment to form beliefs. The beliefs can be used to realize fixed desires of the agent.","PeriodicalId":318973,"journal":{"name":"2019 Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob)","volume":"171 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133632963","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

Creative Problem Solving by Robots Using Action Primitive Discovery 机器人使用动作原始发现创造性地解决问题

2019 Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) Pub Date : 2019-08-01 DOI: 10.1109/DEVLRN.2019.8850711

E. Gizzi, Mateo Guaman Castro, J. Sinapov

引用次数: 11

Within Reach? Learning to touch objects without prior models 的范围内吗?在没有模型的情况下学习触摸物体

2019 Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) Pub Date : 2019-08-01 DOI: 10.1109/DEVLRN.2019.8850702

François De La Bourdonnaye, Céline Teulière, T. Chateau, J. Triesch

引用次数: 3