Semi-parametric Gaussian process for robot system identification
Semi-parametric Gaussian process for robot system identification
复制标题
机器人系统辨识的半参数高斯过程
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
J. Movellan
中科院分区：
文献类型：
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作者：
Tingfan Wu;J. Movellan
仿生机器人的控制如此困难的原因之一是我们没有足够准确的系统动力学数学模型。最近用于系统识别的非参数机器学习方法已显示出良好的前景，当应用于复杂的机器人系统识别问题时，其性能优于参数化数学模型。不幸的是，当应用于训练数据集未密集覆盖的状态空间区域时，非参数方法表现不佳。随着状态空间的增长，这个问题变得尤为重要。参数方法非常有效地使用可用数据，但另一方面，它们仅提供实际系统动力学的粗略近似值。在实践中，参数数学模型与其物理实现之间的系统偏差导致控制律无法利用机器人的顺应性和复杂动力学。在这里，我们提出了一种机器人系统识别方法，称为半参数高斯过程（SGP），它巧妙地结合了参数和非参数方法的优点。欠驱动机器人系统识别问题的计算机模拟和物理实现显示出非常有希望的结果。我们还展示了 SGP 对任意复杂度的铰接式树结构机器人的适用性。在所有实验中，SGP 的性能显着优于之前的参数和非参数方法以及之前结合这两种方法的方法。
One reason why control of biomimetic robots is so difficult is the fact that we do not have sufficiently accurate mathematical models of their system dynamics. Recent nonparametric machine learning approaches to system identification have shown good promise, outperforming parameterized mathematical models when applied to complex robot system identification problems. Unfortunately, non-parametric methods perform poorly when applied to regions of the state space that are not densely covered by the training dataset. This problem becomes particularly critical as the state space grows. Parametric methods use the available data very efficiently but, on the flip side, they only provide crude approximations to the actual system dynamics. In practice the systematic deviations between the parametric mathematical model and its physical realization results in control laws that do not take advantage of the compliance and complex dynamics of the robot. Here we present an approach to robot system identification, named Semi-Parametric Gaussian Processes (SGP), that elegantly combines the advantages of parametric and non-parametric approaches. Computer simulations and a physical implementation of an underactuated robot system identification problem show very promising results. We also demonstrate the applicability of SGP to articulated tree-structured robots of arbitrary complexity. In all experiments, SGP significantly out-performed previous parametric and non-parametric approaches as well as previous methods for combining the two approaches.