Robotic manipulation is critical for admitting robotic agents to various
application domains, like intelligent assistance. A major challenge therein is
the effective 6DoF grasping of objects in cluttered environments from any
viewpoint without requiring additional scene exploration. We introduce
$\textit{NeuGraspNet}$, a novel method for 6DoF grasp detection that leverages
recent advances in neural volumetric representations and surface rendering. Our
approach learns both global (scene-level) and local (grasp-level) neural
surface representations, enabling effective and fully implicit 6DoF grasp
quality prediction, even in unseen parts of the scene. Further, we reinterpret
grasping as a local neural surface rendering problem, allowing the model to
encode the interaction between the robot's end-effector and the object's
surface geometry. NeuGraspNet operates on single viewpoints and can sample
grasp candidates in occluded scenes, outperforming existing implicit and
semi-implicit baseline methods in the literature. We demonstrate the real-world
applicability of NeuGraspNet with a mobile manipulator robot, grasping in open
spaces with clutter by rendering the scene, reasoning about graspable areas of
different objects, and selecting grasps likely to succeed without colliding
with the environment. Visit our project website:
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本文介绍了一种名为 NeuGraspNet 的新型方法，它使用神经立体表示和表面渲染的最新进展，学习了全局和本地的神经表面表示，并使用局部神经表面渲染问题对抓取进行重新解释，能够在遮挡的场景中操作，并展示了在移动机械手中应用的效果。