Learning movement primitive libraries through probabilistic segmentation
Learning movement primitive libraries through probabilistic segmentation
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DOI：
10.1177/0278364917713116
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发表时间：
2017-07-01
影响因子：
9.2
通讯作者：
Peters, Jan
中科院分区：
文献类型：
--
作者：
Lioutikov, Rudolf;Neumann, Gerhard;Peters, Jan
运动原语是编码和执行运动的一种成熟方法。虽然基元本身已被广泛研究，但运动基元库的概念尚未得到类似的关注。运动基元库表示智能体的技能集。可以对原语进行查询和排序，以解决特定的任务。这项工作的目标是将未标记的演示分割成一组具有代表性的原语。我们提出的方法不同于目前的方法，利用往往被忽视的，包含在演示和原语之间的相互依赖关系的片段进行编码。通过利用这种相互依赖性，我们表明，我们可以提高分割和运动原语库。基于概率推理，我们的新方法段的演示，同时学习运动原语的概率表示。我们证明了我们的方法在两个真实的机器人应用。首先，机器人将不同字母的序列分割成一个库，解释观察到的轨迹。其次，机器人将椅子装配任务的演示分割到运动基元库中。该库随后被用于组装椅子的顺序不存在的示范。
Movement primitives are a well-established approach for encoding and executing movements. While the primitives themselves have been extensively researched, the concept of movement primitive libraries has not received similar attention. Libraries of movement primitives represent the skill set of an agent. Primitives can be queried and sequenced in order to solve specific tasks. The goal of this work is to segment unlabeled demonstrations into a representative set of primitives. Our proposed method differs from current approaches by taking advantage of the often neglected, mutual dependencies between the segments contained in the demonstrations and the primitives to be encoded. By exploiting this mutual dependency, we show that we can improve both the segmentation and the movement primitive library. Based on probabilistic inference our novel approach segments the demonstrations while learning a probabilistic representation of movement primitives. We demonstrate our method on two real robot applications. First, the robot segments sequences of different letters into a library, explaining the observed trajectories. Second, the robot segments demonstrations of a chair assembly task into a movement primitive library. The library is subsequently used to assemble the chair in an order not present in the demonstrations.