A Multi-Step Grasping Framework for Zero-Shot Object Detection in Everyday Environments Based on Lightweight Foundational General Models

Achieving object grasping in everyday environments by leveraging the powerful generalization capabilities of foundational general models while enhancing their deployment efficiency within robotic control systems represents a key challenge for service robots. To address the application environments and hardware resource constraints of household robots, a Three-step Pipeline Grasping Framework (TPGF) is proposed for zero-shot object grasping. The framework operates on the principle of “object perception–object point cloud extraction–grasping pose determination” and requires no training or fine-tuning. We integrate advanced foundational models into the Object Perception Module (OPM) to maximize zero-shot generalization and develop a novel Point Cloud Extraction Method (PCEM) based on Depth Information Suppression (DIS) to enable targeted grasping from complex scenes. Furthermore, to significantly reduce hardware overhead and accelerate deployment, a Saturated Truncation strategy based on relative information entropy is introduced for high-precision quantization, resulting in the highly efficient model, EntQ-EdgeSAM. Experimental results on public datasets demonstrate the superior inspection generalization of the combined foundational models compared to task-specific baselines. The proposed Saturated Truncation strategy achieves 3–21% higher quantization accuracy than symmetric uniform quantization, leading to 3.5% model file compression and 95% faster inference speed for EntQ-EdgeSAM. Grasping experiments confirm that the TPGF achieves robust recognition accuracy and high grasping success rates in zero-shot object grasping tasks within replicated everyday environments, proving its practical value and efficiency for real-world robotic deployment.