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Real-Time Target-Oriented Grasping Framework for Resource-Constrained Robots.

Dongxiao Han1, Haorong Li1, Yuwen Li1

  • 1Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 201900, China.

Sensors (Basel, Switzerland)
|January 28, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an efficient framework for real-time target-oriented grasping on robots with limited computing power. The system achieves high success rates in various scenarios, enabling mobile robot applications.

Keywords:
efficient frameworkgeometry-based grasp correctionresource-constrained platformsstructured pruningtarget-oriented grasping

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Area of Science:

  • Robotics and Automation
  • Computer Vision
  • Artificial Intelligence

Background:

  • Target-oriented grasping is crucial for household and industrial robots.
  • Deploying grasping systems on mobile robots is challenging due to computational constraints.

Purpose of the Study:

  • To develop an efficient framework for real-time target-oriented grasping on resource-constrained platforms.
  • To support both click-based grasping for unknown objects and category-based grasping for known objects.

Main Methods:

  • Compressed YOLOv8 for reduced model complexity and maintained detection accuracy.
  • GR-ConvNetv2 for grasp pose generation, refined by MobileSAMv2 masks.
  • A geometry-based correction module for improved grasp pose accuracy.

Main Results:

  • Achieved 98.8% grasp success on the Cornell dataset and 95.8% on the Jacquard dataset.
  • Demonstrated over 90% success in real-world cluttered and single-object scenarios.
  • Real-time performance: 67 ms (click-based) and 75 ms (category-based) per frame.

Conclusions:

  • The proposed framework offers high grasping accuracy and robust performance.
  • The efficient design enables deployment on mobile and resource-constrained robots.