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Related Experiment Video

Updated: May 12, 2026

Frame-by-Frame Video Analysis of Idiosyncratic Reach-to-Grasp Movements in Humans

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Do Similar Objects Have Similar Grasp Positions?

Qi Sun¹, Lili He¹

¹School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China.

Sensors (Basel, Switzerland)

|December 17, 2024

Summary

This summary is machine-generated.

This study quantitatively analyzes if similar objects have similar grasp positions. Findings suggest a positive correlation, confirming the principle for robotic grasping applications.

Keywords:

deep neutral network (DNN)grasping shape descriptor

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Published on: April 21, 2023

Area of Science:

Robotics
Computer Vision
Machine Learning

Background:

Shape similarity is a common reference for robotic grasp prediction.
Quantitative analysis of the relationship between object shape and grasp similarity is lacking.

Purpose of the Study:

To quantitatively analyze the correlation between object shape similarity and grasp position similarity.
To confirm or disprove the hypothesis that similar objects possess similar grasp positions.

Main Methods:

Constructed a similarity-estimation plane (SE-Plane) to visualize object and grasp similarity.
Utilized classical shape descriptors and deep neural network (DNN) architectures for object similarity.
Employed intersection-over-union (IoU) of grasp anchors to measure grasp similarity.

Main Results:

Demonstrated a general proportionality between object similarity and grasp anchor IoU on the SE-Plane.
Identified specific primitive shapes as effective references for grasp prediction.
Validated the SE-Plane approach on a realistic object dataset.

Conclusions:

Concluded that similar objects generally have similar grasp positions, validating the core hypothesis.
The SE-Plane offers a novel framework for assessing object-grasp similarity relationships.
Findings support the use of shape similarity as a reliable indicator in robotic grasping.