Robotic manipulation systems operating in complex environments rely on
perception systems that provide information about the geometry (pose and 3D
shape) of the objects in the scene along with other semantic information such
as object labels. This information is then used for choosing the feasible
grasps on relevant objects. In this paper, we present a novel method to provide
this geometric and semantic information of all objects in the scene as well as
feasible grasps on those objects simultaneously. The main advantage of our
method is its speed as it avoids sequential perception and grasp planning
steps. With detailed quantitative analysis, we show that our method delivers
competitive performance compared to the state-of-the-art dedicated methods for
object shape, pose, and grasp predictions while providing fast inference at 30
frames per second speed.

本文介绍了一种新的方法，通过感知系统提供场景中所有物体的几何和语义信息，同时提供这些物体的可行抓取，以加速机器人操作的复杂度。通过详细的定量分析，我们展示了我们的方法通过在 30 帧 / 秒速度下提供竞争性能，以比较专门用于物体形状、姿态和抓取预测的最新技术方法。