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Related Experiment Video

Updated: Jan 9, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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

Ruibo Li1, Tie Zhang1, Yanbiao Zou1

  • 1School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, China.

Sensors (Basel, Switzerland)
|December 11, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a Three-step Pipeline Grasping Framework (TPGF) for efficient, zero-shot object grasping in household robots. The framework enhances foundational models for robust robotic control in everyday environments.

Keywords:
image segmentationmodel quantizationobject detectionrobotic graspingzero-shot object

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computer Vision

Background:

  • Service robots face challenges deploying foundational models for object grasping in resource-constrained household environments.
  • Existing methods often require extensive training or fine-tuning, limiting real-world applicability.

Purpose of the Study:

  • To propose an efficient, zero-shot object grasping framework for household robots.
  • To enhance the generalization and deployment efficiency of foundational models in robotic control.

Main Methods:

  • Introduced a Three-step Pipeline Grasping Framework (TPGF) comprising Object Perception Module (OPM), Point Cloud Extraction Method (PCEM) with Depth Information Suppression (DIS), and grasp pose determination.
  • Developed EntQ-EdgeSAM, a highly efficient model using Saturated Truncation for high-precision quantization, reducing hardware overhead.
  • Integrated advanced foundational models into the OPM for maximized zero-shot generalization.

Main Results:

  • TPGF demonstrated robust recognition accuracy and high grasping success rates in zero-shot object grasping tasks.
  • The Saturated Truncation strategy improved quantization accuracy by 3-21% and achieved 95% faster inference speed for EntQ-EdgeSAM.
  • Foundational models integrated into OPM showed superior generalization compared to task-specific baselines.

Conclusions:

  • The TPGF offers a practical and efficient solution for zero-shot object grasping in everyday environments for service robots.
  • EntQ-EdgeSAM significantly enhances model efficiency, enabling practical deployment on resource-limited household robots.
  • The framework's zero-shot capabilities and efficiency prove valuable for real-world robotic applications.