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Robotics Dexterous Grasping: The Methods Based on Point Cloud and Deep Learning.

Haonan Duan^1,2,3, Peng Wang^1,3,4, Yayu Huang^1,3

¹The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.

Frontiers in Neurorobotics

|June 28, 2021

Summary

This summary is machine-generated.

This review categorizes robotic dexterous grasping methods using point cloud and deep learning. It introduces a novel generation-evaluation framework for classifying these advanced robotic manipulation techniques.

Keywords:

deep learning dexterous grasping point cloud review robotics

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Area of Science:

Robotics
Artificial Intelligence
Computer Vision

Background:

Dexterous manipulation, particularly grasping, is essential for robots to perform human-like tasks.
Robotic grasping advancements are crucial for applications in daily life and industrial settings.

Purpose of the Study:

To provide a comprehensive review of robotic dexterous grasping methods.
To classify methods based on point cloud and deep learning approaches.
To offer a guideline for researchers and developers in the field.

Main Methods:

Categorization of methods from three perspectives.
Focus on a novel generation-evaluation framework for classification.
Inclusion of classifications based on learning modes and applications.

Main Results:

A structured review of state-of-the-art robotic grasping techniques.
Introduction of a new classification scheme centered on a generation-evaluation framework.
Overview of different learning modes and application areas for robotic grasping.

Conclusions:

The review establishes a clear framework for understanding robotic dexterous grasping.
It highlights the importance of point cloud and deep learning in advancing robotic manipulation.
The work serves as a valuable resource for the robotics research community.