LiteGrasp: A Light Robotic Grasp Detection via Semi-Supervised Knowledge Distillation

Grasping detection from single images in robotic applications poses a significant challenge. While contemporary deep learning techniques excel, their success often hinges on large annotated datasets and intricate network architectures. In this letter, we present LiteGrasp, a novel semi-supervised li...

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Bibliographic Details
Published in:IEEE robotics and automation letters 2024-09, Vol.9 (9), p.7995-8002
Main Authors: Peng, Linpeng, Cai, Rongyao, Xiang, Jingyang, Zhu, Junyu, Liu, Weiwei, Gao, Wang, Liu, Yong
Format: Article
Language:English
Subjects:
Data mining
Data models
Deep learning
Feature extraction
Grasp detection
Grasping
Grasping (robotics)
Image filters
knowledge distillation
Labels
Robot arms
robotic grasping
Robotics
Robots
semi-supervised learning
Semisupervised learning
Training
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Summary:Grasping detection from single images in robotic applications poses a significant challenge. While contemporary deep learning techniques excel, their success often hinges on large annotated datasets and intricate network architectures. In this letter, we present LiteGrasp, a novel semi-supervised lightweight framework purpose-built for grasp detection, eliminating the necessity for exhaustive supervision and intricate networks. Our approach uses a limited amount of labeled data via a knowledge distillation method, introducing HRGrasp-Net, a model with high efficiency for extracting features and largely based on HRNet. We incorporate pseudo-label filtering within a mutual learning model set within a teacher-student paradigm. This enhances the transference of data from images with labels to those without. Additionally, we introduce the streamlined Lite HRGrasp-Net, acting as the student network which gains further distillation knowledge using a multi-level fusion cascade originating from HRGrasp-Net. Impressively, LiteGrasp thrives with just a fraction (4.3%) of HRGrasp-Net's original model size, and with limited labeled data relative to total data (25% ratio) across all benchmarks, regularly outperforming solely supervised and semi-supervised models. Taking just 6 ms for execution, LiteGrasp showcases exceptional accuracy (99.99% and 97.21% on Cornell and Jacquard data sets respectively), as well as an impressive 95.3% rate of success in grasping when deployed using a 6DoF UR5e robotic arm. These highlights underscore the effectiveness and efficiency of LiteGrasp for grasp detection, even under resource-limited conditions.
ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2024.3436336