论文标题
一个像孩子一样算的机器人：计数和指向的发展模型
A robot that counts like a child: a developmental model of counting and pointing
论文作者
论文摘要
在本文中，引入了一种能够计算真实项目的新型神经企业模型。该模型使我们能够研究实施方案与数值认知之间的相互作用。这是由能够图像处理和顺序任务性能的深层神经网络组成的，以及提供实施例的机器人平台 - ICUB类人体机器人。该网络是使用机器人相机和关节的本体感受信号的图像进行训练的。训练有素的模型能够计算一组项目，并同时指向它们。我们研究指向计数过程的影响，并将我们的结果与与儿童研究的结果进行比较。本文提出了几种培训方法，所有这些方法都使用预训练的常规，使网络在计数培训之前就可以获得指向和数字朗诵的能力（从1到10）。研究了计数设置大小和与物体距离的影响。获得的计数绩效结果表明与人类研究的结果相似。
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.
