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An End-to-End Computationally Lightweight Vision-Based Grasping System for Grocery Items
Thanavin Mansakul1, Gilbert Tang1, Phil Webb1
1Centre for Robotics and Assembly, Faculty of Engineering and Applied Sciences, Cranfield University, Bedford MK43 0AL, UK.
Sensors (Basel, Switzerland)
|September 13, 2025
Summary
This study introduces an efficient vision-based grasping framework for mobile manipulators. The system achieves fast and accurate grasp detection, enabling practical robotic applications with high success rates.
Area of Science:
- Robotics
- Computer Vision
- Machine Learning
Background:
- Vision-based grasping for mobile manipulators faces challenges in perception, efficiency, and deployment.
- Existing methods often lack computational efficiency for real-time applications.
Purpose of the Study:
- To develop a computationally lightweight, end-to-end grasp detection framework for mobile manipulators.
- To enable accurate manipulation by mapping image coordinates to the robot frame.
Main Methods:
- Integrated object detection, pose estimation, and grasp point prediction.
- Developed a coordinate transformation model from image to robot frame.
- Created a benchmark and dataset for pick-and-pack grocery tasks.
Main Results:
- Achieved average execution time under 5 seconds on an edge device.
- Demonstrated 100% success rate on Level 1 and 96% on Level 2 of the benchmark.
- Reported an average compute-to-speed ratio of 0.0130, indicating energy efficiency.
Conclusions:
- The proposed framework is a practical, robust, and efficient solution for vision-based grasping.
- It is suitable for lightweight robotic applications in real-world environments.
- The system addresses key challenges in mobile manipulator perception and efficiency.
Keywords:
end-to-end grasp detectionlightweight computationmachine visionmobile manipulatorobject detectionobject pose estimationvision-based grasping systemMore Related Videos
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