A Novel Data mining method for next-generation robots

在这项研究中，我们挑战了对下一代机器人至关重要的新型建模技术的发展。我们假设我们的建模技术有两个功能。功能1检测建模所需的短数据，通过操作机器人生成建模所需的新数据。函数2是从大量采样数据中只选择建模所需的数据的函数。在利用视觉信息控制三自由度机械臂的过程中，研究了基于核主成分分析的方法，该方法利用非线性映射函数将高维学习数据压缩到低维数据。

In this study, we challenge development of the novel modeling technique that is essential to a next-generation robot. We assume that our modeling technique has two functions. Function 1 detects the data which are short for modeling, and generates new data necessary for modeling by operating a robot. Function 2 is a function to choose only data necessary for modeling from a large amount of sampled data. When we controlled a 3-DOF manipulator using visual information, we examined the method based on the Kernel PCA method which can compress high-dimensional learning data using a non-linear mapping function to the low-dimensional data.