论文标题
通过负担坐标框架在混乱中以操纵为导向的物体感知
Manipulation-Oriented Object Perception in Clutter through Affordance Coordinate Frames
论文作者
论文摘要
为了在非结构化环境中实现强大的操作，机器人应该能够将操作动作推广到新颖的对象实例。例如，要倒酒并提供饮料，机器人应该能够识别出负担该任务的新颖容器。最重要的是，机器人应该能够操纵这些新颖的容器以完成任务。为了实现这一目标，我们旨在提供对物体负担的强大而广义的看法，并提供可靠的操纵的相关操纵姿势。在这项工作中，我们结合了负担能力和类别级姿势的概念，并引入了负担坐标框架（ACF）。使用ACF，我们代表每个对象类别的单个负担零件以及它们之间的兼容性，其中每个部分都与一个零件类别级别的姿势相关联，以进行机器人操纵。在我们的实验中，我们证明了ACF优于对象检测的最先进方法以及对象部分的类别级姿势估计。我们进一步证明了通过模拟环境中的实验，ACF对机器人操纵任务的适用性。
In order to enable robust operation in unstructured environments, robots should be able to generalize manipulation actions to novel object instances. For example, to pour and serve a drink, a robot should be able to recognize novel containers which afford the task. Most importantly, robots should be able to manipulate these novel containers to fulfill the task. To achieve this, we aim to provide robust and generalized perception of object affordances and their associated manipulation poses for reliable manipulation. In this work, we combine the notions of affordance and category-level pose, and introduce the Affordance Coordinate Frame (ACF). With ACF, we represent each object class in terms of individual affordance parts and the compatibility between them, where each part is associated with a part category-level pose for robot manipulation. In our experiments, we demonstrate that ACF outperforms state-of-the-art methods for object detection, as well as category-level pose estimation for object parts. We further demonstrate the applicability of ACF to robot manipulation tasks through experiments in a simulated environment.
