Fast and efficient incremental learning for high-dimensional movement systems
Fast and efficient incremental learning for high-dimensional movement systems
复制标题
高维运动系统快速高效的增量学习
DOI：
--
复制
发表时间：
2000
期刊：
影响因子：
--
通讯作者：
S. Schaal
中科院分区：
文献类型：
--
作者：
S. Vijayakumar;S. Schaal
我们介绍了一种新的算法，局部加权投影回归（LWPR），增量实时学习的非线性函数，特别是有用的自主实时机器人控制，需要内部模型的动力学，运动学，或其他功能的问题。在其核心，LWPR使用局部线性模型，由输入空间中选定方向的少量单变量回归构成，以实现分段线性函数逼近。LWPR的突出特性是，它i）使用基于增量训练的二阶学习方法快速学习，ii）使用统计上合理的随机交叉验证来学习，iii）仅基于局部信息调整其局部加权核以避免干扰问题，iv）具有在输入数量上线性的计算复杂度，以及v）可以处理大量可能冗余和/或不相关的输入，如在用于学习拟人机器人手臂的逆动力学的高达50维数据集的评估中所示。据我们所知，这是第一个将所有这些特性联合收割机结合起来的增量神经网络学习方法，并且非常适合于机器人中的复杂在线学习问题。
We introduce a new algorithm, locally weighted projection regression (LWPR), for incremental real-time learning of nonlinear functions, as particularly useful for problems of autonomous real-time robot control that requires internal models of dynamics, kinematics, or other functions. At its core, LWPR uses locally linear models, spanned by a small number of univariate regressions in selected directions in input space, to achieve piecewise linear function approximation. The outstanding properties of LWPR are that it i) learns rapidly with second order learning methods based on incremental training, ii) uses statistically sound stochastic cross validation to learn iii) adjusts its local weighting kernels based on only local information to avoid interference problems, iv) has a computational complexity that is linear in the number of inputs, and v) can deal with a large number of possibly redundant and/or irrelevant inputs, as shown in evaluations with up to 50 dimensional data sets for learning the inverse dynamics of an anthropomorphic robot arm. To our knowledge, this is the first incremental neural network learning method to combine all these properties and that is well suited for complex online learning problems in robotics.