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首页> 外文期刊>International Journal of Social Robotics >A Novel Reinforcement-Based Paradigm for Children to Teach the Humanoid Kaspar Robot
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A Novel Reinforcement-Based Paradigm for Children to Teach the Humanoid Kaspar Robot

机译:一种新的基于加强的儿童范式,用于教育人形kaspar机器人

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This paper presents a contribution aiming at testing novel child-robot teaching schemes that could be used in future studies to support the development of social and collaborative skills of children with autism spectrum disorders (ASD). We present a novel experiment where the classical roles are reversed: in this scenario the children are the teachers providing positive or negative reinforcement to the Kaspar robot in order for it to learn arbitrary associations between different toy names and the locations where they are positioned. The objective is to stimulate interaction and collaboration between children while teaching the robot, and also provide them tangible examples to understand that sometimes learning requires several repetitions. To facilitate this game, we developed a reinforcement learning algorithm enabling Kaspar to verbally convey its level of uncertainty during the learning process, so as to better inform the children about the reasons behind its successes and failures. Overall, 30 typically developing (TD) children aged between 7 and 8 (19 girls, 11 boys) and 9 children with ASD performed 25 sessions (16 for TD; 9 for ASD) of the experiment in groups, and managed to teach Kaspar all associations in 2 to 7 trials. During the course of study Kaspar only made rare unexpected associations (2 perseverative errors and 2 win-shifts, within a total of 314 trials), primarily due to exploratory choices, and eventually reached minimal uncertainty. Thus, the robot's behaviour was clear and consistent for the children, who all expressed enthusiasm in the experiment.
机译:本文介绍了针对测试新的儿童机器人教学计划的贡献,这些计划可用于未来的研究,以支持自闭症谱系统(ASD)的社会和协作技能的发展。我们提出了一种新的实验,竞争典型的角色:在这种情况下,孩子们是为Kaspar机器人提供积极或负面加固的教师,以便学习不同玩具名称之间的任意关联和它们所在的位置。目标是在教授机器人时刺激儿童之间的互动和协作,并为他们提供有形的例子,以了解有时学习需要几次重复。为了促进这场比赛,我们开发了一种强化学习算法,使亚卡斯普能够在学习过程中口头传达其不确定性水平,从而更好地通知孩子们的成功和失败背后的原因。总体而言,30名典型的发展中国家(TD)儿童在7到8名(19名女孩,11名男孩)和9名儿童的儿童,亚本亚当(ASD为9号TD; 9为TD; 9),并设法教授Kaspar全部2至7项试验中的协会。在研究过程中,Kaspar只有罕见的意想不到的协会(2个坚持不懈的错误和2个连续班,总共314名试验中),主要是由于探索性选择,最终达到了最小的不确定性。因此,机器人的行为是对儿童的清晰和一致,他们都表达了在实验中的热情。

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