用于机器人操作规划的铰接物体神经场表示
Neural Field Representations of Articulated Objects for Robotic Manipulation Planning
AI总结：
提出一种铰接物体的神经场表示（NFR），可直接从少量图像正向模拟运动并用于轨迹优化规划，支持形状重建等辅助任务，在合成数据训练下实现对真实世界未见同类物体的机器人操作。
摘要：
传统的操作规划方法依赖环境的显式几何模型，将给定任务表述为优化问题。然而，从原始传感器输入推断准确模型本身就是一个难题，特别是对于铰接物体（例如壁橱、抽屉）。在本文中，我们提出了一种铰接物体的 Neural Field Representation (NFR)（神经场表示），能够直接从图像进行操作规划。具体而言，在拍摄新铰接物体的几张照片后，我们可以对其可能的运动进行正向模拟，从而直接将该神经模型用于轨迹优化规划。此外，该表示可用于形状重建、语义分割和图像渲染，在训练和泛化过程中提供了强大的监督信号。我们表明，仅在合成图像上训练的模型，在仿真和真实图像中均能为同类未见物体提取有意义的表示。进一步地，我们证明该表示能够直接基于图像在现实世界中实现对铰接物体的机器人操作。
英文摘要：
Traditional approaches for manipulation planning rely on an explicit geometric model of the environment to formulate a given task as an optimization problem. However, inferring an accurate model from raw sensor input is a hard problem in itself, in particular for articulated objects (e.g., closets, drawers). In this paper, we propose a Neural Field Representation (NFR) of articulated objects that enables manipulation planning directly from images. Specifically, after taking a few pictures of a new articulated object, we can forward simulate its possible movements, and, therefore, use this neural model directly for planning with trajectory optimization. Additionally, this representation can be used for shape reconstruction, semantic segmentation and image rendering, which provides a strong supervision signal during training and generalization. We show that our model, which was trained only on synthetic images, is able to extract a meaningful representation for unseen objects of the same class, both in simulation and with real images. Furthermore, we demonstrate that the representation enables robotic manipulation of an articulated object in the real world directly from images.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
