论文标题
IFOR：机器人对象重排的迭代流量最小化
IFOR: Iterative Flow Minimization for Robotic Object Rearrangement
论文作者
论文摘要
对于非结构化环境中各种各样的现实世界机器人应用程序，视觉重新排列是一个至关重要的问题。我们建议机器人对象重排的IFOR，迭代流量最小化，这是针对原始场景和最终场景的RGBD映像的对象重新排列的具有挑战性问题的端到端方法。首先，我们学习基于筏的光流模型，以估算纯粹从合成数据的对象的相对转换。然后将该流程用于迭代最小化算法中，以实现以前看不见的对象的准确定位。至关重要的是，我们表明我们的方法适用于混乱的场景和现实世界，而仅对合成数据进行训练。视频可在https://imankgoyal.github.io/ifor.html上找到。
Accurate object rearrangement from vision is a crucial problem for a wide variety of real-world robotics applications in unstructured environments. We propose IFOR, Iterative Flow Minimization for Robotic Object Rearrangement, an end-to-end method for the challenging problem of object rearrangement for unknown objects given an RGBD image of the original and final scenes. First, we learn an optical flow model based on RAFT to estimate the relative transformation of the objects purely from synthetic data. This flow is then used in an iterative minimization algorithm to achieve accurate positioning of previously unseen objects. Crucially, we show that our method applies to cluttered scenes, and in the real world, while training only on synthetic data. Videos are available at https://imankgoyal.github.io/ifor.html.
