Using probabilistic movement primitives in robotics
Using probabilistic movement primitives in robotics
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DOI：
10.1007/s10514-017-9648-7
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发表时间：
2018-03-01
影响因子：
3.5
通讯作者：
Neumann, Gerhard
中科院分区：
文献类型：
--
作者：
Paraschos, Alexandros;Daniel, Christian;Neumann, Gerhard
运动基元是模块化运动表示和生成的一个良好范例。它们提供了一个数据驱动的运动表示，并支持泛化到新的情况下，时间调制，原语和控制器的排序，用于在物理系统上执行原语。然而，虽然许多MP框架展示了其中的一些属性，但需要一个统一的框架，以原则性的方式实现所有这些属性。在本文中，我们表明，这一目标可以通过使用概率表示来实现。我们的方法模型从随机运动中学习的轨迹分布。概率操作，如条件反射，可以用来实现对新情况的概括，或者以原则性的方式联合收割机和混合运动。我们推导出一个随机反馈控制器，再现编码的变化的运动和耦合的自由度的机器人。我们评估和比较我们的方法在几个模拟和真实的机器人场景。
Movement Primitives are a well-established paradigm for modular movement representation and generation. They provide a data-driven representation of movements and support generalization to novel situations, temporal modulation, sequencing of primitives and controllers for executing the primitive on physical systems. However, while many MP frameworks exhibit some of these properties, there is a need for a unified framework that implements all of them in a principled way. In this paper, we show that this goal can be achieved by using a probabilistic representation. Our approach models trajectory distributions learned from stochastic movements. Probabilistic operations, such as conditioning can be used to achieve generalization to novel situations or to combine and blend movements in a principled way. We derive a stochastic feedback controller that reproduces the encoded variability of the movement and the coupling of the degrees of freedom of the robot. We evaluate and compare our approach on several simulated and real robot scenarios.