2022 IEEE International Conference on Development and Learning (ICDL)最新文献

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Validating a Cortisol-Inspired Framework for Human-Robot Interaction with a Replication of the Still Face Paradigm 验证一个受皮质醇启发的人机交互框架与静止面孔范式的复制

2022 IEEE International Conference on Development and Learning (ICDL) Pub Date : 2022-04-07 DOI: 10.1109/ICDL53763.2022.9962184

Sara Mongile, Ana Tanevska, F. Rea, A. Sciutti

{"title":"Validating a Cortisol-Inspired Framework for Human-Robot Interaction with a Replication of the Still Face Paradigm","authors":"Sara Mongile, Ana Tanevska, F. Rea, A. Sciutti","doi":"10.1109/ICDL53763.2022.9962184","DOIUrl":"https://doi.org/10.1109/ICDL53763.2022.9962184","url":null,"abstract":"When interacting with others in our everyday life, we prefer the company of those who share with us the same desire of closeness and intimacy (or lack thereof), since this determines if our interaction will be more o less pleasant. This sort of compatibility can be inferred by our innate attachment style. The attachment style represents our characteristic way of thinking, feeling and behaving in close relationship, and other than behaviourally, it can also affect us biologically via our hormonal dynamics. When we are looking how to enrich humanrobot interaction (HRI), one potential solution could be enabling robots to understand their partners’ attachment style, which could then improve the perception of their partners and help them behave in an adaptive manner during the interaction. We propose to use the relationship between the attachment style and the cortisol hormone, to endow the humanoid robot iCub with an internal cortisol inspired framework that allows it to infer participant’s attachment style by the effect of the interaction on its cortisol levels (referred to as R-cortisol). In this work, we present our cognitive framework and its validation during the replication of a well-known paradigm on hormonal modulation in human-human interaction (HHI) - the Still Face paradigm.","PeriodicalId":274171,"journal":{"name":"2022 IEEE International Conference on Development and Learning (ICDL)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121646285","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

Disentangling Patterns and Transformations from One Sequence of Images with Shape-invariant Lie Group Transformer 用形状不变李群变换从一序列图像中解纠缠模式和变换

2022 IEEE International Conference on Development and Learning (ICDL) Pub Date : 2022-03-21 DOI: 10.1109/ICDL53763.2022.9962232

Takumi Takada, Wataru Shimaya, Y. Ohmura, Y. Kuniyoshi

{"title":"Disentangling Patterns and Transformations from One Sequence of Images with Shape-invariant Lie Group Transformer","authors":"Takumi Takada, Wataru Shimaya, Y. Ohmura, Y. Kuniyoshi","doi":"10.1109/ICDL53763.2022.9962232","DOIUrl":"https://doi.org/10.1109/ICDL53763.2022.9962232","url":null,"abstract":"An effective way to model the complex real world is to view the world as a composition of basic components of objects and transformations. Although humans through development understand the compositionality of the real world, it is extremely difficult to equip robots with such a learning mechanism. In recent years, there has been significant research on autonomously learning representations of the world using the deep learning; however, most studies have taken a statistical approach, which requires a large number of training data. Contrary to such existing methods, we take a novel algebraic approach for representation learning based on a simpler and more intuitive formulation that the observed world is the combination of multiple independent patterns and transformations that are invariant to the shape of patterns. Since the shape of patterns can be viewed as the invariant features against symmetric transformations such as translation or rotation, we can expect that the patterns can naturally be extracted by expressing transformations with symmetric Lie group transformers and attempting to reconstruct the scene with them. Based on this idea, we propose a model that disentangles the scenes into the minimum number of basic components of patterns and Lie transformations from only one sequence of images, by introducing the learnable shape-invariant Lie group transformers as transformation components. Experiments show that given one sequence of images in which two objects are moving independently, the proposed model can discover the hidden distinct objects and multiple shape-invariant transformations that constitute the scenes.","PeriodicalId":274171,"journal":{"name":"2022 IEEE International Conference on Development and Learning (ICDL)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129405004","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

Accelerating the Learning of TAMER with Counterfactual Explanations 用反事实解释加速TAMER的学习

2022 IEEE International Conference on Development and Learning (ICDL) Pub Date : 2021-08-03 DOI: 10.1109/ICDL53763.2022.9962222

Jakob Karalus, F. Lindner

引用次数: 1

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