Event driven bio-inspired attentive system for the iCub humanoid robot on SpiNNaker
Event driven bio-inspired attentive system for the iCub humanoid robot on SpiNNaker
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DOI：
10.1088/2634-4386/ac6b50
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Giulia D’Angelo;Adam Perrett;Massimiliano Iacono;S. Furber;C. Bartolozzi
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文献类型：
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作者：
Giulia D’Angelo;Adam Perrett;Massimiliano Iacono;S. Furber;C. Bartolozzi
注意力将观察者的目光引向感兴趣的项目，仅允许对场景的选定区域进行详细分析。机器人可以利用场景中特征的感知组织来引导其注意力，以更好地理解其环境。目前自下而上的注意力模型与标准RGB相机一起工作，需要大量的时间来以基于帧的方式检测最显著的项目。事件驱动摄像机是一种创新技术，可异步检测场景中的对比度变化，具有高时间分辨率和低延迟。我们提出了一个新的神经形态管道利用事件驱动的相机的异步输出生成显着性图的场景。为了进一步减少延迟，神经形态注意力模型在SpiNNaker（一个专用的神经形态平台）上的尖峰神经网络中实现。所提出的实现已与其生物启发的GPU对应物进行了比较，并已对地面实况固定地图进行了基准测试。该系统成功地检测到场景中的项目，产生显着图与GPU实现。异步流水线实现了平均16 ms的延迟，以产生可用的显著性图。
Attention leads the gaze of the observer towards interesting items, allowing a detailed analysis only for selected regions of a scene. A robot can take advantage of the perceptual organisation of the features in the scene to guide its attention to better understand its environment. Current bottom–up attention models work with standard RGB cameras requiring a significant amount of time to detect the most salient item in a frame-based fashion. Event-driven cameras are an innovative technology to asynchronously detect contrast changes in the scene with a high temporal resolution and low latency. We propose a new neuromorphic pipeline exploiting the asynchronous output of the event-driven cameras to generate saliency maps of the scene. In an attempt to further decrease the latency, the neuromorphic attention model is implemented in a spiking neural network on SpiNNaker, a dedicated neuromorphic platform. The proposed implementation has been compared with its bio-inspired GPU counterpart, and it has been benchmarked against ground truth fixational maps. The system successfully detects items in the scene, producing saliency maps comparable with the GPU implementation. The asynchronous pipeline achieves an average of 16 ms latency to produce a usable saliency map.