1. WasteGAN: Data Augmentation for Robotic Waste Sorting through Generative Adversarial Networks
- Author
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Bacchin, Alberto, Barcellona, Leonardo, Terreran, Matteo, Ghidoni, Stefano, Menegatti, Emanuele, and Kiyokawa, Takuya
- Subjects
Computer Science - Robotics ,Computer Science - Computer Vision and Pattern Recognition - Abstract
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, Comment: Accepted at 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024)
- Published
- 2024