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: https://sites.google.com/view/neugraspnet

翻译：机器人操作对于机器人智能助手等应用领域至关重要，其主要挑战在于无需额外场景探索，即可从任意视角有效抓取杂乱环境中的物体。我们提出$\textit{NeuGraspNet}$，一种利用神经体积表示与表面渲染最新进展的六自由度抓取检测新方法。该方法同时学习全局（场景级）与局部（抓取级）神经表面表示，即使在场景的未观测部分也能实现高效且完全隐式的六自由度抓取质量预测。此外，我们将抓取重解释为局部神经表面渲染问题，使模型能够编码机器人末端执行器与物体表面几何之间的交互关系。NeuGraspNet基于单视角输入，可在遮挡场景中采样抓取候选，性能优于现有隐式与半隐式基线方法。我们通过移动操作机器人在开放空间中进行杂乱环境下的实际应用验证：通过渲染场景推理不同物体的可抓取区域，选择无碰撞的高成功率抓取策略。项目网站：https://sites.google.com/view/neugraspnet