Long-Short Term Memory Networks for Modelling Embodied Mathematical Cognition in Robots
Long-Short Term Memory Networks for Modelling Embodied Mathematical Cognition in Robots
复制标题
用于机器人数学认知建模的长短期记忆网络
DOI：
10.1109/ijcnn.2018.8489140
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
A. D. Nuovo
中科院分区：
文献类型：
--
作者：
A. D. Nuovo
数学能力可以赋予机器人必要的抽象和符号处理能力，这是自然语言理解等高级认知功能所必需的。但是，到目前为止，在机器人中建立数学认知模型的尝试还很少。本文介绍了一种用于模拟认知机器人中简单的数学运算的长-短期记忆网络的实验评估。为此，机器人模型在来自手指计数的本体感觉信息和MNIST数据集的手写数字之间建立了关联。在实践中，该模型同时执行两个任务：它识别序列中的手写数字并对它们求和。结果表明，与手指的关联可以提高机器人的精度，正如在儿童中观察到的那样。此外，机器人会犯不成比例的五分错误，这与对儿童和成年人的研究中观察到的错误相似，因此提供了证据，支持了这些错误是由于使用五指计数系统的假设。
Mathematical competence can endow robots with the necessary capability for abstract and symbolic processing, which is required for higher cognitive functions such as natural language understanding. But, so far, only few attempts have been made to model mathematical cognition in robots. This paper presents an experimental evaluation of the Long- Short Term Memory networks for modeling the simple mathematical operation of single-digits addition in a cognitive robot. To this end, the robotic model creates an association between the proprioceptive information from finger counting and the handwritten digits of the MNIST dataset. In practice, the model executes two tasks concurrently: it recognizes the handwritten digits in a sequence and sums them. The results show that the association with fingers can improve the robot precision, as observed in children. Also, the robot makes a disproportionate number of split-five errors similarly to what observed in studies with children and adults, hence giving evidence to support the hypothesis that these errors are due the use of a five-fingers counting system.
影响因子：
3.8
作者：
Moeller K;Martignon L;Wessolowski S;Engel J;Nuerk HC
通讯作者：
Nuerk HC