Robust Loss Functions for Object Grasping under Limited Ground Truth
Preprint
Abstract
Object grasping is a crucial technology enabling robots to perceive and interact with the environment sufficiently. However, in practical applications, researchers are faced with missing or noisy ground truth while training the convolutional neural network, which decreases the accuracy of the model. Therefore, different loss functions are proposed to deal with these problems to improve the accuracy of the neural network. For missing ground truth, a new predicted category probability method is defined for unlabeled samples, which works effectively in conjunction with the pseudo-labeling method. Furthermore, for noisy ground truth, a symmetric loss function is introduced to resist the corruption of label noises. The proposed loss functions are powerful, robust, and easy to use. Experimental results based on the typical grasping neural network show that our method can improve performance by 2 to 13 percent.Keywords
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This publication has 8 references indexed in Scilit:
- DefGraspNets: Grasp Planning on 3D Fields with Graph Neural Nets2023
- CMG-Net: An End-to-End Contact-based Multi-Finger Dexterous Grasping Network2023
- DCBT-Net: Training Deep Convolutional Neural Networks With Extremely Noisy LabelsIEEE Access, 2020
- Grasping in the Wild: Learning 6DoF Closed-Loop Grasping From Low-Cost DemonstrationsIEEE Robotics and Automation Letters, 2020
- Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition From a Domain Adaptation Perspective2020
- GraspNet: An Efficient Convolutional Neural Network for Real-time Grasp Detection for Low-powered Devices2018
- Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collectionThe International Journal of Robotics Research, 2017
- Deep learning for detecting robotic graspsThe International Journal of Robotics Research, 2015