像孩子一样数数的机器人：数数和指点的发展模型
A robot that counts like a child: a developmental model of counting and pointing
摘要：
本文介绍了一种新的神经机器人模型，能够计数的真实项目。该模型使我们能够研究体现和数字认知之间的相互作用。这是由一个深度神经网络能够图像处理和顺序任务的性能，和一个机器人平台提供的实施例-iCub人形机器人。该网络使用来自机器人摄像头的图像和来自其关节的本体感受信号进行训练。经过训练的模型能够对一组项目进行计数，并同时指向它们。我们调查指向计数过程的影响，并将我们的结果与儿童研究的结果进行比较。本文提出了几种训练方法，它们都使用预训练例程，允许网络在计数训练之前获得指向和数字背诵（从1到10）的能力。计算集的大小和距离的对象的影响进行了研究。所获得的计数性能的结果与人类研究的结果相似。
英文摘要：
In this paper, a novel neuro-robotics model capable of counting real items is introduced. The model allows us to investigate the interaction between embodiment and numerical cognition. This is composed of a deep neural network capable of image processing and sequential tasks performance, and a robotic platform providing the embodiment - the iCub humanoid robot. The network is trained using images from the robot's cameras and proprioceptive signals from its joints. The trained model is able to count a set of items and at the same time points to them. We investigate the influence of pointing on the counting process and compare our results with those from studies with children. Several training approaches are presented in this paper all of them uses pre-training routine allowing the network to gain the ability of pointing and number recitation (from 1 to 10) prior to counting training. The impact of the counted set size and distance to the objects are investigated. The obtained results on counting performance show similarities with those from human studies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
