Stability-Aware Closed-Loop Recovery for Robotic Grasping in Complex Simulation Environments

Abstract

Language-guided robotic grasping has made significant progress in semantic understanding, but existing methods often rely on open-loop execution strategies and struggle to handle physical disturbances such as object collisions, target displacement, and transportation slippage. To address this problem, this paper proposes a stability-aware dynamic recovery mechanism, named SADR. Based on a multi-threaded decoupled architecture, SADR decouples semantic planning, target tracking, and execution control, and constructs a two-stage stability criterion through pre-closure displacement checking and post-closure force/current feedback verification. When target instability, missed grasping, or slippage is detected, the system performs local trajectory correction and re-grasping based on real-time tracking results, without restarting global semantic planning. Experiments in PyBullet show that SADR significantly improves the grasping success rate under high-density disturbance scenarios while reducing the average task completion time. This study provides an effective closed-loop recovery solution for improving the reliability of robotic grasping tasks in complex simulation environments.