WasteGAN: Data Augmentation for Robotic Waste Sorting through Generative Adversarial Networks
<|reference_start|>WasteGAN: Data Augmentation for Robotic Waste Sorting through Generative Adversarial Networks: Robotic waste sorting poses significant challenges in both perception and manipulation, given the extreme variability of objects that should be recognized on a cluttered conveyor belt. While deep learning has proven effective in solving complex tasks, the necessity for extensive data collection and labeling limits its applicability in real-world scenarios like waste sorting. To tackle this issue, we introduce a data augmentation method based on a novel GAN architecture called wasteGAN. The proposed method allows to increase the performance of semantic segmentation models, starting from a very limited bunch of labeled examples, such as few as 100. The key innovations of wasteGAN include a novel loss function, a novel activation function, and a larger generator block. Overall, such innovations helps the network to learn from limited number of examples and synthesize data that better mirrors real-world distributions. We then leverage the higher-quality segmentation masks predicted from models trained on the wasteGAN synthetic data to compute semantic-aware grasp poses, enabling a robotic arm to effectively recognizing contaminants and separating waste in a real-world scenario. Through comprehensive evaluation encompassing dataset-based assessments and real-world experiments, our methodology demonstrated promising potential for robotic waste sorting, yielding performance gains of up to 5.8\% in picking contaminants. The project page is available at https://github.com/bach05/wasteGAN.git<|reference_end|>
@article{bacchin2024wastegan:,
    title={WasteGAN: Data Augmentation for Robotic Waste Sorting through Generative
  Adversarial Networks},
    author={Alberto Bacchin, Leonardo Barcellona, Matteo Terreran, Stefano
  Ghidoni, Emanuele Menegatti, Takuya Kiyokawa},
    journal={arXiv preprint arXiv:2409.16999},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.16999},
    primaryClass={cs.RO cs.CV}
}