Flashner H, Beuter A, Boettger C
Department of Mechanical Engineering, University of Southern California, Los Angeles 90089-1453.
Biol Cybern. 1989;60(4):277-84. doi: 10.1007/BF00204125.
In this study we combine the representation of motion by a finite number of hardwired functions with parameter optimization to model learning during a stepping motion. Representation of experimental kinematic data by a finite number of predetermined functions and undetermined coefficients was analyzed. Least squares approximation was used to represent experimental data of stepping motions over obstacles of different heights. Functional relationships between coefficients and obstacles heights were also obtained. Learning of stepping over an obstacle was then formulated as a finite dimensional optimization problem. The pattern of foot path, and joint angles trajectories obtained by this learning model, were then compared to the experimental data. The results of the data fitting analysis and of the optimization process as a model for motion learning, indicate that motion can be adequately represented by a set of hardwired functions, and a finite number of task dependent coefficients.
在本研究中，我们将通过有限数量的硬连线函数表示运动与参数优化相结合，以对踏步运动期间的学习进行建模。分析了用有限数量的预定函数和待定系数表示实验运动学数据的情况。使用最小二乘法逼近表示跨越不同高度障碍物的踏步运动的实验数据。还获得了系数与障碍物高度之间的函数关系。然后将跨越障碍物的学习表述为一个有限维优化问题。接着将通过该学习模型获得的足部路径模式和关节角度轨迹与实验数据进行比较。作为运动学习模型的数据拟合分析和优化过程的结果表明，运动可以由一组硬连线函数和有限数量的任务相关系数充分表示。
