Learning from multiple demonstrations using trajectory-aware non-rigid registration with applications to deformable object manipulation
Learning from multiple demonstrations using trajectory-aware non-rigid registration with applications to deformable object manipulation
复制标题
使用轨迹感知非刚性配准以及可变形对象操作的应用程序从多个演示中学习
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
P. Abbeel
中科院分区：
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作者：
Alex X. Lee;Abhishek Gupta;Henry Lu;S. Levine;P. Abbeel
通过非刚性点云配准的演示学习是学习操纵各种可变形物体的有效工具。然而，大多数使用非刚性配准来转移演示轨迹的方法都假设测试和演示场景在结构上非常相似，任何变化都可以通过非线性变换来解释。在现实世界的任务与混乱和分心的对象，这个假设是不现实的。在这项工作中，我们证明了一种自动感知的非刚性配准方法，该方法使用多个演示将配准过程集中在与任务相关的点上，可以有效地处理比以前的方法更大的视觉变化。我们证明，这种方法实现了上级概括几个具有挑战性的任务，包括毛巾折叠和抓物体在一个盒子里包含无关的干扰。
Learning from demonstration by means of non-rigid point cloud registration is an effective tool for learning to manipulate a wide range of deformable objects. However, most methods that use non-rigid registration to transfer demonstrated trajectories assume that the test and demonstration scene are structurally very similar, with any variation explained by a non-linear transformation. In real-world tasks with clutter and distractor objects, this assumption is unrealistic. In this work, we show that a trajectory-aware non-rigid registration method that uses multiple demonstrations to focus the registration process on points that are relevant to the task can effectively handle significantly greater visual variation than prior methods that are not trajectory-aware. We demonstrate that this approach achieves superior generalization on several challenging tasks, including towel folding and grasping objects in a box containing irrelevant distractors.