Trajectory Learning for Robot Programming by Demonstration Using Hidden Markov Model and Dynamic Time Warping
Trajectory Learning for Robot Programming by Demonstration Using Hidden Markov Model and Dynamic Time Warping
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DOI：
10.1109/tsmcb.2012.2185694
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发表时间：
2012-08-01
影响因子：
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通讯作者：
Janabi-Sharifi, Farrokh
中科院分区：
文献类型：
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作者：
Vakanski, Aleksandar;Mantegh, Iraj;Janabi-Sharifi, Farrokh
本文的主要目的是开发一种有效的方法，学习和再现的复杂轨迹的机器人编程演示。利用隐马尔可夫模型对演示轨迹进行编码，并利用关键点的概念生成广义轨迹。关键点的确定是基于演示轨迹中位置和速度的显著变化。使用多维动态时间规整算法对所得到的轨迹关键点序列进行时间对齐，并通过对聚类的关键点进行平滑样条插值来获得广义轨迹。我们所提出的方法的主要优点是利用轨迹的关键点，从所有的演示生成一个广义的轨迹。此外，在整个演示集的关键点的集群的可变性被用于分配加权系数，从而导致在一个概括的过程，占再现的轨迹的不同部分的相关性。实验验证了两个不同层次的复杂性的轨迹的方法。
The main objective of this paper is to develop an efficient method for learning and reproduction of complex trajectories for robot programming by demonstration. Encoding of the demonstrated trajectories is performed with hidden Markov model, and generation of a generalized trajectory is achieved by using the concept of key points. Identification of the key points is based on significant changes in position and velocity in the demonstrated trajectories. The resulting sequences of trajectory key points are temporally aligned using the multidimensional dynamic time warping algorithm, and a generalized trajectory is obtained by smoothing spline interpolation of the clustered key points. The principal advantage of our proposed approach is utilization of the trajectory key points from all demonstrations for generation of a generalized trajectory. In addition, variability of the key points' clusters across the demonstrated set is employed for assigning weighting coefficients, resulting in a generalization procedure which accounts for the relevance of reproduction of different parts of the trajectories. The approach is verified experimentally for trajectories with two different levels of complexity.