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Manipulation and Analysis

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

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Published on: April 21, 2023

Recent Data Sets on Object Manipulation: A Survey.

Yongqiang Huang¹, Matteo Bianchi², Minas Liarokapis³

¹1 Department of Computer Science and Engineering, University of South Florida , Tampa, Florida.

|December 20, 2016

Summary

This summary is machine-generated.

This review organizes grasping and object manipulation datasets from the last decade. It provides insights for selecting existing datasets or creating new ones, aiding neuroscience and robotics research.

Keywords:

data collection data set learning robotic grasping robotic manipulation robotics

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Area of Science:

Robotics and Neuroscience
Human Behavior Analysis

Background:

Data sets are vital for machine learning, evaluation, and understanding human behavior.
Selecting appropriate datasets for object manipulation and grasping is challenging due to data variety.

Purpose of the Study:

To systematically review and organize object manipulation and grasping datasets published in the last 10 years.
To provide a comprehensive overview of available datasets, including their modalities, activities, and annotations.

Main Methods:

Literature review of datasets related to object manipulation and grasping.
Analysis and categorization of datasets based on modalities, activities, and annotations.
Comparative analysis and summarization of reviewed datasets.

Main Results:

A curated list of object manipulation and grasping datasets from the past decade.
Detailed reports on the characteristics of each dataset, including data types and annotation richness.
A comparative analysis highlighting the strengths and weaknesses of different datasets for specific applications.

Conclusions:

The review offers guidance on utilizing existing datasets for research in neuroscience and robotics.
Best practices and suggestions for the creation of new, high-quality datasets are proposed.
This work aims to foster mutual inspiration between neuroscience and robotics by organizing crucial data resources.