Visual attention and object naming in humanoid robots using a bio-inspired spiking neural network
Visual attention and object naming in humanoid robots using a bio-inspired spiking neural network
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DOI：
10.1016/j.robot.2018.02.010
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发表时间：
2018-06-01
影响因子：
4.3
通讯作者：
Cangelosi，Angelo
中科院分区：
文献类型：
--
作者：
Garcia，Daniel Hernandez;Adams，Samantha;Cangelosi，Angelo
行为和计算神经科学、认知机器人以及大规模神经网络的硬件实现方面的最新进展，为加速理解大脑功能和基于大脑控制系统的交互式机器人系统的设计提供了机会。在行动和语言学习领域尤其如此，因为这一领域的科学和技术取得了重大发展。在这项工作中，我们描述了如何将基于神经解剖学的视觉注意尖峰神经网络扩展为单词学习能力，并与iCub人形机器人集成以演示注意力引导的物体命名。在仿真和真实的iCub机器人平台上进行了实验，取得了成功的结果。当场景中同时出现视觉和文字刺激时，iCub机器人能够将一个标签与一个“首选”方向的物体联系起来，并注意到所述物体，从而命名它。学习完成后，只要有视觉输入，即使物体从原来的位置移动了，或者有其他物体作为干扰物存在，也能成功地回忆起物体的名字。
Recent advances in behavioural and computational neuroscience, cognitive robotics, and in the hardware implementation of large-scale neural networks, provide the opportunity for an accelerated understanding of brain functions and for the design of interactive robotic systems based on brain-inspired control systems. This is especially the case in the domain of action and language learning, given the significant scientific and technological developments in this field. In this work we describe how a neuroanatomically grounded spiking neural network for visual attention has been extended with a word learning capability and integrated with the iCub humanoid robot to demonstrate attention-led object naming. Experiments were carried out with both a simulated and a real iCub robot platform with successful results. The iCub robot is capable of associating a label to an object with a ‘preferred’ orientation when visual and word stimuli are presented concurrently in the scene, as well as attending to said object, thus naming it. After learning is complete, the name of the object can be recalled successfully when only the visual input is present, even when the object has been moved from its original position or when other objects are present as distractors.