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Related Experiment Video

Updated: Dec 13, 2025

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

Predicting precision grip grasp locations on three-dimensional objects.

Lina K Klein¹, Guido Maiello¹, Vivian C Paulun¹

¹Department of Experimental Psychology, Justus Liebig University Giessen, Giessen, Germany.

Plos Computational Biology

|August 5, 2020

Summary

This summary is machine-generated.

Humans systematically grasp unfamiliar 3D objects using a predictive model. This model integrates multiple factors like shape and weight, accurately forecasting grasp strategies for diverse objects.

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Last Updated: Dec 13, 2025

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

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Published on: April 21, 2023

Measurement of Spatial Stability in Precision Grip

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Published on: June 4, 2020

Design and Use of an Apparatus for Presenting Graspable Objects in 3D Workspace

Design and Use of an Apparatus for Presenting Graspable Objects in 3D Workspace

Published on: August 8, 2019

Area of Science:

Robotics
Human-Computer Interaction
Biomechanics

Background:

Object manipulation is fundamental to human interaction.
Effective grasping relies on selecting optimal contact points from numerous possibilities.

Purpose of the Study:

To develop a normative model predicting human precision grasping of unfamiliar 3D objects.
To understand the systematic strategies underlying human grasp selection.

Main Methods:

Collected extensive behavioral data tracking finger and thumb movements during object grasping.
Developed a computational model integrating five cost functions: force closure, torque, natural grasp axis, grasp aperture, and visibility.

Main Results:

Human grasps were systematic and consistent across participants and repetitions.
The model accurately predicted human grasps on novel objects, even with complex geometries.
Model performance was robust to parameter perturbations.

Conclusions:

The findings offer a unified account of human grasping for objects of varying 3D properties.
The developed model provides significant insights into the predictive nature of human motor control for manipulation tasks.