Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Three-Dimensional Force System:Problem Solving01:30

Three-Dimensional Force System:Problem Solving

709
A three-dimensional force system refers to a scenario in which three forces act simultaneously in three different directions. This type of problem is commonly encountered in physics and engineering, where it is necessary to calculate the resultant force on the system, which can then be used to predict or analyze the behavior of the object or structure under consideration.
To solve a three-dimensional force system, first resolve each force into its respective scalar components. Do this using...
709

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Assessing Treatment Effects in Observational Data With Missing Confounders: A Comparative Study of Practical Doubly-Robust and Traditional Missing Data Methods.

Statistics in medicine·2026
Same author

Unraveling the genetic association between autoimmune thyroid diseases and idiopathic inflammatory myopathies in the European population.

Medicine·2025
Same author

Quantization-based chained privacy-preserving federated learning.

Scientific reports·2025
Same author

FLT3LG modulates the infiltration of immune cells and enhances the efficacy of anti-PD-1 therapy in lung adenocarcinoma.

BMC cancer·2025
Same author

Thermoplastic Polyureas with Excellent Mechanical Properties Synthesized From CO<sub>2</sub>-Based Oligourea.

Macromolecular rapid communications·2025
Same author

Statistical inference on the relative risk following covariate-adaptive randomization.

Biometrics·2025

Related Experiment Video

Updated: Aug 13, 2025

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
09:41

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

Published on: April 21, 2023

1.7K

A Practical Multi-Stage Grasp Detection Method for Kinova Robot in Stacked Environments.

Xuefeng Dong1, Yang Jiang1, Fengyu Zhao1

  • 1Faculty of Robot Science and Engineering, Northeastern University, Shenyang 110169, China.

Micromachines
|January 21, 2023
PubMed
Summary

A new Multi-stage network for multi-object grasp detection algorithm (MMD) improves robotic grasp detection in cluttered environments. This method achieves state-of-the-art precision, enhancing robot manipulation capabilities.

Keywords:
Kinova robotVMRDgrasp detectionmulti-stage networkmulti-taskstack scenarios

More Related Videos

Design and Use of an Apparatus for Presenting Graspable Objects in 3D Workspace
09:11

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

Published on: August 8, 2019

5.8K
Frame-by-Frame Video Analysis of Idiosyncratic Reach-to-Grasp Movements in Humans
10:51

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

Published on: January 15, 2018

8.4K

Related Experiment Videos

Last Updated: Aug 13, 2025

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
09:41

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

Published on: April 21, 2023

1.7K
Design and Use of an Apparatus for Presenting Graspable Objects in 3D Workspace
09:11

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

Published on: August 8, 2019

5.8K
Frame-by-Frame Video Analysis of Idiosyncratic Reach-to-Grasp Movements in Humans
10:51

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

Published on: January 15, 2018

8.4K

Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Robotic grasp detection is crucial for manipulation tasks.
  • Detecting objects and grasp positions in cluttered environments presents significant challenges.
  • Existing methods struggle with accuracy in complex, stacked scenarios.

Purpose of the Study:

  • To propose a novel algorithm for accurate multi-object grasp detection.
  • To enhance the precision of object and grasp position identification for robots.
  • To address the difficulties in grasp detection within stacked environments.

Main Methods:

  • Introduced the Multi-stage network for multi-object grasp detection algorithm (MMD).
  • MMD utilizes a deep convolutional neural network as a feature extractor to generate initial regions of interest (ROIs).
  • A multi-stage refiner iteratively regresses ROIs for precise object and grasp detection.

Main Results:

  • MMD demonstrated superior grasp detection performance compared to existing methods.
  • Achieved a state-of-the-art recognition precision of 76.71% mAPg on the VMRD dataset.
  • Experimental tests confirmed the method's feasibility on a Kinova robot.

Conclusions:

  • The proposed MMD algorithm significantly improves grasp detection accuracy.
  • MMD offers a viable solution for precise robotic manipulation in complex environments.
  • The method shows practical applicability for real-world robotic systems.