Submit

Login Login

Register Register

Home / Journals / CMC / Online First / doi:10.32604/cmc.2025.073442
Journal Menu
Special Issues
Table of Content

All issues

Online First

2026

2025

2024

2023

2022

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

Open Access

ARTICLE

A Robot Grasp Detection Method Based on Neural Architecture Search and Its Interpretability Analysis

Lu Rong1,#, Manyu Xu2,3,#, Wenbo Zhu2,*, Zhihao Yang2,3, Chao Dong1,4,5, Yunzhi Zhang2,3, Kai Wang1,2, Bing Zheng1,4,5
1 South China Sea Marine Survey Center, Ministry of Natural Resources of the People’s Republic of China, Guangzhou, 510300, China
2 School of Mechanical Engineering and Automation, Foshan University, Foshan, 528200, China
3 Guangdong Provincial Key Laboratory of Industrial Intelligent Inspection Technology, Foshan University, Foshan, 528000, China
4 Key Laboratory of Marine Environmental Survey Technology and Application, Ministry of Natural Resources of the People’s Republic of China, Guangzhou, 510300, China
5 Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519000, China
* Corresponding Author: Wenbo Zhu. Email: email
# These authors contributed equally to this work

Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.073442

Received 18 September 2025; Accepted 25 November 2025; Published online 22 December 2025

Abstract

Deep learning has become integral to robotics, particularly in tasks such as robotic grasping, where objects often exhibit diverse shapes, textures, and physical properties. In robotic grasping tasks, due to the diverse characteristics of the targets, frequent adjustments to the network architecture and parameters are required to avoid a decrease in model accuracy, which presents a significant challenge for non-experts. Neural Architecture Search (NAS) provides a compelling method through the automated generation of network architectures, enabling the discovery of models that achieve high accuracy through efficient search algorithms. Compared to manually designed networks, NAS methods can significantly reduce design costs, time expenditure, and improve model performance. However, such methods often involve complex topological connections, and these redundant structures can severely reduce computational efficiency. To overcome this challenge, this work puts forward a robotic grasp detection framework founded on NAS. The method automatically designs a lightweight network with high accuracy and low topological complexity, effectively adapting to the target object to generate the optimal grasp pose, thereby significantly improving the success rate of robotic grasping. Additionally, we use Class Activation Mapping (CAM) as an interpretability tool, which captures sensitive information during the perception process through visualized results. The searched model achieved competitive, and in some cases superior, performance on the Cornell and Jacquard public datasets, achieving accuracies of 98.3% and 96.8%, respectively, while sustaining a detection speed of 89 frames per second with only 0.41 million parameters. To further validate its effectiveness beyond benchmark evaluations, we conducted real-world grasping experiments on a UR5 robotic arm, where the model demonstrated reliable performance across diverse objects and high grasp success rates, thereby confirming its practical applicability in robotic manipulation tasks.

Keywords

Robotics; grasping detection; neural architecture search; neural network interpretability

  • 234

    View

  • 38

    Download

  • 0

    Like

Related articles
Copyright© 2026 Tech Science Press
© 1997-2026 TSP (Henderson, USA) unless otherwise stated

Share Link