DiNBV-Grasp: Real-Time Distance-Aware Two-Stage Next-Best-View for Robotic Grasping

Next-Best-View (NBV) planning is widely adopted for robotic grasping in cluttered and occluded environments. However, existing methods typically assume that a single observation at an arbitrary camera–object distance is sufficient for accurate grasp prediction. This overlooks a critical factor: grasp perception quality is highly sensitive to viewing distance, and the optimal distance varies significantly across object categories and scales. To address this limitation, we propose DiNBVGrasp, a real-time, distance-aware two-stage NBV framework. In Stage I, we distill a large vision-language model (VLM) into a lightweight planner for global semantic NBV exploration, resolving occlusions and generating an initial grasp hypothesis with low-latency inference. In Stage II, we introduce a local distance-aware refinement mechanism that explicitly models the relationship between viewing distance and grasp success probability. During manipulator approach, a lightweight scoring network evaluates intermediate observations and selects the most informative one for final grasp prediction. Extensive simulation and real-world experiments demonstrate that DiNBV-Grasp consistently improves grasp success rates across diverse objects, particularly for small and occluded targets, while preserving real-time performance, highlighting the importance of distance-aware perception in NBV-based grasping. Our code is available at https://github.com/XieZilongAI/DiNBV-Grasp.