General robot grasping in clutter requires the ability to synthesize grasps
that work for previously unseen objects and that are also robust to physical
interactions, such as collisions with other objects in the scene. In this work,
we design and train a network that predicts 6 DOF grasps from 3D scene
information gathered from an on-board sensor such as a wrist-mounted depth
camera. Our proposed Volumetric Grasping Network (VGN) accepts a Truncated
Signed Distance Function (TSDF) representation of the scene and directly
outputs the predicted grasp quality and the associated gripper orientation and
opening width for each voxel in the queried 3D volume. We show that our
approach can plan grasps in only 10 ms and is able to clear 92% of the objects
in real-world clutter removal experiments without the need for explicit
collision checking. The real-time capability opens up the possibility for
closed-loop grasp planning, allowing robots to handle disturbances, recover
from errors and provide increased robustness. Code is available at
this https URL

本研究介紹了一種使用體積把握網絡（VGN）的方法，該網絡通過使用由手腕安裝式深度相機收集的 3D 場景信息，從過去未見過的物體中預測並設計出能夠穩健地擷取物體的抓取方法。