# Robust Loss Functions for Object Grasping under Limited Ground Truth

arXiv: https://arxiv.org/abs/2409.05742 · HTML: https://mlsanity.com/paper/2409.05742/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 29 of its 45 references that are in the map.
- First posted on arXiv 9 Sep 2024.

## Builds on (most cited first, 29 of 29)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Self-training with Noisy Student improves ImageNet classification](https://mlsanity.com/paper/1911.04252/) (2019)
- [QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation](https://mlsanity.com/paper/1806.10293/) (2018)
- [MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels](https://mlsanity.com/paper/1712.05055/) (2017)
- [Symmetric Cross Entropy for Robust Learning with Noisy Labels](https://mlsanity.com/paper/1908.06112/) (2019)
- [Using Pre-Training Can Improve Model Robustness and Uncertainty](https://mlsanity.com/paper/1901.09960/) (2019)
- [Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection](https://mlsanity.com/paper/1603.02199/) (2016)
- [Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experience](https://mlsanity.com/paper/1810.05687/) (2018)
- [Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition from a Domain Adaptation Perspective](https://mlsanity.com/paper/2003.10780/) (2020)
- [Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image Matching](https://mlsanity.com/paper/1710.01330/) (2017)
- [Deep Learning for Detecting Robotic Grasps](https://mlsanity.com/paper/1301.3592/) (2013)
- [Grasping in the Wild:Learning 6DoF Closed-Loop Grasping from Low-Cost Demonstrations](https://mlsanity.com/paper/1912.04344/) (2019)
- [Sim-to-Real Robot Learning from Pixels with Progressive Nets](https://mlsanity.com/paper/1610.04286/) (2016)
- [Curriculum Loss: Robust Learning and Generalization against Label Corruption](https://mlsanity.com/paper/1905.10045/) (2019)
- [Combating Label Noise in Deep Learning Using Abstention](https://mlsanity.com/paper/1905.10964/) (2019)
- [PointNetGPD: Detecting Grasp Configurations from Point Sets](https://mlsanity.com/paper/1809.06267/) (2018)
- [Real-Time Grasp Detection Using Convolutional Neural Networks](https://mlsanity.com/paper/1412.3128/) (2014)
- [GraspNeRF: Multiview-based 6-DoF Grasp Detection for Transparent and Specular Objects Using Generalizable NeRF](https://mlsanity.com/paper/2210.06575/) (2022)
- [Antipodal Robotic Grasping using Generative Residual Convolutional Neural Network](https://mlsanity.com/paper/1909.04810/) (2019)
- [Learning Dexterous Manipulation for a Soft Robotic Hand from Human Demonstration](https://mlsanity.com/paper/1603.06348/) (2016)
- [Edge Grasp Network: A Graph-Based SE(3)-invariant Approach to Grasp Detection](https://mlsanity.com/paper/2211.00191/) (2022)
- [High precision grasp pose detection in dense clutter](https://mlsanity.com/paper/1603.01564/) (2016)
- [Fully Convolutional Grasp Detection Network with Oriented Anchor Box](https://mlsanity.com/paper/1803.02209/) (2018)
- [Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks](https://mlsanity.com/paper/1611.06430/) (2016)
- [Keypoint-GraspNet: Keypoint-based 6-DoF Grasp Generation from the Monocular RGB-D input](https://mlsanity.com/paper/2209.08752/) (2022)
- [Contact-Rich Manipulation of a Flexible Object based on Deep Predictive Learning using Vision and Tactility](https://mlsanity.com/paper/2112.06442/) (2021)
- [DefGraspNets: Grasp Planning on 3D Fields with Graph Neural Nets](https://mlsanity.com/paper/2303.16138/) (2023)
- [CMG-Net: An End-to-End Contact-Based Multi-Finger Dexterous Grasping Network](https://mlsanity.com/paper/2303.13182/) (2023)

## Abstract (arXiv)

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.