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Posture recognition analysis during human-robot imitation learning

机译:人体机器人模仿学习期间的姿态识别分析

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Imitation is a powerful paradigm for learning new actions and tasks and is currently investigated in several research domains such as developmental psychology or robotics [8]. For example, learning by demonstration is employed in robotics to teach new skills and tasks to robots [1] [2]. The dynamics of coupling of perception of one partner to the action of the other, and vice-and-versa, has been exploited to characterize social traits [9]. In particular, in the case of strong coupling such as imitation, research in developmental science has shown that imitation could be considered a social referencing mechanism [7]. Recently, identification of social traits during human-robot imitation learning has been successfully demonstrated. For example a social signature emerges from a neural network architecture and distinguishes typical children and children with Autism Spectrum Disorders (ASD). The key idea of this approach is to learn the mapping between the perception of postures performed by the children and the actions performed by the robot. A perception-action architecture based on neural network (PerAc [6]) is used to associate the perception with action [3]. Interestingly, this approach generalizes to person's identity recognition by the analysis of the dynamics of the social signature (novelty detection procedure) [5].
机译:模仿是一种强大的范例,用于学习新的行动和任务,目前在发育心理学或机器人等几个研究领域进行了调查[8]。例如,通过演示学习在机器人学中,为机器人提供新的技能和任务[1] [2]。对一个伙伴对另一个伙伴的动作的耦合动态,以及反之亦然,已经被利用来表征社会特征[9]。特别是,在仿制诸如仿制的强烈耦合的情况下,发展科学的研究表明,模仿可以被认为是社会参考机制[7]。最近,已经成功地证明了在人机仿制学习期间的社会性状的识别。例如,社会签名从神经网络建筑中出现,并区分典型的儿童和自闭症谱系统(ASD)。这种方法的关键思想是学习儿童执行的姿势的感知与机器人执行的动作之间的映射。基于神经网络(PERAC [6])的感知行动架构用于将感知与Action [3]联系起来。有趣的是,这种方法通过分析社会签名(新颖性检测程序)的动态来推广人的身份识别[5]。

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