Active-Perceptive Language-Oriented Grasp Policy for Heavily Cluttered Scenes

Abstract

Language-guided robotic grasping in cluttered environments presents significant challenges due to severe occlusions and complex scene structures, which often hinder accurate target localization. Existing approaches typically suffer from limited observational capabilities, resulting in suboptimal exploration of the target object. In this paper, we propose a novel Active-Perceptive Language-Oriented Grasp Policy (APeG) for heavily cluttered scenes. APeG develops an active perception scheme in the grasp pipeline via an occlusion-aware, semantic-guided viewpoint optimization strategy, enabling efficient exploration of cluttered scenes. In addition, a grasp-wise Reinforcement Learning (RL) policy is proposed to select robust grasp poses. Extensive real-world experiments validate the effectiveness of APeG, demonstrating significant improvements in both task success rate and operational efficiency over existing baselines, highlighting its potential for practical deployment in language-conditioned robotic manipulation.