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Updated: Aug 2, 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

Learning-based robotic grasping: A review.

Zhen Xie¹, Xinquan Liang², Canale Roberto¹

¹Advanced Remanufacturing and Technology Centre (ARTC), Agency for Science, Technology and Research (ASTAR), Singapore, Singapore.

Frontiers in Robotics and AI

|April 21, 2023

Summary

This summary is machine-generated.

Learning-based robotic grasping offers flexible solutions for industries like logistics and food delivery, enabling automation for unknown objects. This review highlights advancements and challenges in AI-enabled grasping for adaptable robotic systems.

Keywords:

high mix and low volume learning policy personalization soft gripping tactile sensing versatile grasping

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Area of Science:

Robotics
Artificial Intelligence
Machine Learning

Background:

Personalization technologies drive demand for automated object grasping in logistics, FMCG, and food delivery.
Traditional object recognition methods are insufficient for grasping unknown, variable objects.
High-mix, low-volume manufacturing requires adaptable robotic grasping solutions.

Purpose of the Study:

To review recent advancements in learning-based robotic grasping techniques.
To identify current achievements, gaps, and challenges in AI-enabled grasping.
To survey tactile sensors, robot skin, and sensor feedback for grasping stability.

Main Methods:

Comprehensive literature review of over 150 papers on learning-based robotic grasping.
Analysis of 3D object segmentation and grasping benchmarks.
Market survey of tactile sensors and robot skin.
Review of sensor feedback integration with learning models.

Main Results:

Learning-based approaches enhance robotic grasping for unknown objects with varying shapes and textures.
Soft grippers, trained with learning models, improve adaptability for diverse and fragile items.
AI-enabled grasping shows potential for increased flexibility and adaptability in industrial applications.

Conclusions:

Learning algorithms are crucial for advancing robotic grasping flexibility and adaptability.
Addressing identified gaps is key to wider robotization in relevant industries.
Integration of sensor feedback and advanced materials like soft grippers are promising avenues.