MSP-Grasp: Multiscale Perceptual Framework for 6-DoF Grasping in Cluttered Environments

Autonomous grasping in cluttered environments represents one of the most challenging problems in robotic manipulation. The presence of occlusions and densely packed objects significantly complicates perception and substantially increases the risk of collisions. To address these challenges, we present a novel multiscale perception-based framework for robust robotic grasping in dense, cluttered environments. Our methodology adopts a multiscale progressive perception architecture: First, a global context awareness module analyzes the distribution of viable grasping opportunities, assesses collision risks, and evaluates spatial optimization potential to systematically identify optimal grasping regions. Second, a regional collision prediction module provides intermediate-scale analysis, effectively reducing collision incidents through enhanced spatial awareness. Finally, a local grasp evaluation module refines grasp selection by optimizing stability metrics and predicting the probability of grasp success. Comprehensive experiments across simulated environments and real-world scenarios demonstrate that our approach achieves substantially superior performance compared to existing baselines, confirming its effectiveness and applicability.