GPD-AP: A Grasp Pose-Driven Active Perception Framework for Occlusion-Robust Robotic Manipulation

Abstract

Humans instinctively adjust their viewpoints to resolve occlusions and infer spatial relationships, enabling effective perception and navigation in cluttered environments. This capability, however, remains a significant challenge for robotic systems. To address this, we propose GPD-AP, a novel active perception framework that leverages grasp pose estimation and associated scoring to systematically tackle grasping tasks in occluded and cluttered settings. The core innovation lies in an end-to-end system where a computationally efficient grasp pose estimation module directly informs a Next-Best-View (NBV) planner. This integration shifts the focus from generic scene exploration to a grasp-oriented visual search, guiding the robot to viewpoints that minimize uncertainty about potential grasps. To train and validate GPD-AP, we introduce a simulation reset method capable of generating highly challenging scenes with partially or fully occluded target objects. Experimental results demonstrate that GPD-AP improves grasping success rates by 30% in dense obstacle environments, effectively enabling the transition of target objects from invisible to visible and graspable states. This work marks a significant step towards autonomous and intelligent robotic manipulation in unstructured real-world scenarios.