Local Neural Descriptor Fields: Locally Conditioned Object Representations for Manipulation
Local Neural Descriptor Fields: Locally Conditioned Object Representations for Manipulation
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DOI：
10.1109/icra48891.2023.10160423
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发表时间：
2023-02
期刊：
影响因子：
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通讯作者：
Ethan Chun;Yilun Du;A. Simeonov;Tomas Lozano-Perez;L. Kaelbling
中科院分区：
文献类型：
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作者：
Ethan Chun;Yilun Du;A. Simeonov;Tomas Lozano-Perez;L. Kaelbling
在家庭环境中操作的机器人将看到各种独特和不熟悉的物体。虽然一个系统可以训练其中的许多对象，但预测机器人将看到的所有对象是不可行的。在本文中，我们提出了一种方法来概括从有限数量的演示中获得的对象操作技能，从看不见的形状类别的新对象。我们的方法，局部神经描述符字段（L-NDF），利用神经描述符定义的局部几何形状的对象，有效地转移到新的对象在测试时的操作演示。在这样做时，我们利用对象之间共享的局部几何来产生更通用的操作框架。我们说明了我们的方法在操纵新的对象在新的姿态-无论是在模拟和真实的世界的功效。项目网站、视频和代码：https://elchun.github.io/lndf/。
A robot operating in a household environment will see a wide range of unique and unfamiliar objects. While a system could train on many of these, it is infeasible to predict all the objects a robot will see. In this paper, we present a method to generalize object manipulation skills acquired from a limited number of demonstrations, to novel objects from unseen shape categories. Our approach, Local Neural Descriptor Fields (L-NDF), utilizes neural descriptors defined on the local geometry of the object to effectively transfer manipulation demonstrations to novel objects at test time. In doing so, we leverage the local geometry shared between objects to produce a more general manipulation framework. We illustrate the efficacy of our approach in manipulating novel objects in novel poses - both in simulation and in the real world. Project website, videos, and code: https://elchun.github.io/lndf/.