Movement primitives via optimization
Movement primitives via optimization
复制标题
通过优化的运动原语
DOI：
10.1109/icra.2015.7139510
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
S. Srinivasa
中科院分区：
文献类型：
--
作者：
A. Dragan;Katharina Muelling;J. Bagnell;S. Srinivasa;S. Srinivasa
我们将使演示轨迹适应新的起点和目标配置的问题形式化为希尔伯特轨迹空间上的优化问题：在新的终点约束下，最小化演示与新轨迹之间的距离。我们展示了动态运动基元 (DMP) 的常用版本通过适应演示的方式实现了这种最小化，以适应希尔伯特空间范数的特定选择。对任意规范的泛化使机器人能够为任务选择更合适的规范，并学习如何适应用户的演示。我们的实验表明，这可以显着提高机器人准确概括演示的能力。
We formalize the problem of adapting a demonstrated trajectory to a new start and goal configuration as an optimization problem over a Hilbert space of trajectories: minimize the distance between the demonstration and the new trajectory subject to the new end point constraints. We show that the commonly used version of Dynamic Movement Primitives (DMPs) implement this minimization in the way they adapt demonstrations, for a particular choice of the Hilbert space norm. The generalization to arbitrary norms enables the robot to select a more appropriate norm for the task, as well as learn how to adapt the demonstration from the user. Our experiments show that this can significantly improve the robot's ability to accurately generalize the demonstration.