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：机器人操控对于将智能体引入各类应用领域（如智能辅助）至关重要。其核心挑战在于无需额外场景探索，便能在杂乱环境中从任意视角有效实现物体的六自由度抓取。我们提出了一种名为$\textit{NeuGraspNet}$的新方法，用于六自由度抓取检测，该方法利用了神经体积表示与表面渲染领域的最新进展。我们的方法同时学习全局（场景级）和局部（抓取级）神经表面表示，从而能够在场景中未见区域实现高效且完全隐式的六自由度抓取质量预测。此外，我们将抓取重新诠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。NeuGraspNet基于单视角输入，可对遮挡场景中的抓取候选点进行采样，其性能优于现有文献中的隐式和半隐式基线方法。我们通过在移动操控机器人上的实际应用验证了NeuGraspNet的可行性：机器人通过渲染场景、推理不同物体的可抓取区域，并选择成功率高且避免与环境碰撞的抓取方案，在开放空间中实现杂乱环境下的抓取。访问我们的项目网站：https://sites.google.com/view/neugraspnet