The Collection of a Human Robot Collaboration Dataset for Cooperative Assembly in Glovebox Environments
This addresses the problem of safe human-robot collaboration in industrial settings like glovebox environments, but it is incremental as it focuses on dataset creation rather than new algorithms.
The authors tackled the lack of real-world datasets for hand segmentation in industrial human-robot collaboration by presenting HAGS, a dataset with challenging examples and uncertainty estimations, which they used to evaluate state-of-the-art models and found that synthetic data does not effectively transfer to real-world operations.
Industry 4.0 introduced AI as a transformative solution for modernizing manufacturing processes. Its successor, Industry 5.0, envisions humans as collaborators and experts guiding these AI-driven manufacturing solutions. Developing these techniques necessitates algorithms capable of safe, real-time identification of human positions in a scene, particularly their hands, during collaborative assembly. Although substantial efforts have curated datasets for hand segmentation, most focus on residential or commercial domains. Existing datasets targeting industrial settings predominantly rely on synthetic data, which we demonstrate does not effectively transfer to real-world operations. Moreover, these datasets lack uncertainty estimations critical for safe collaboration. Addressing these gaps, we present HAGS: Hand and Glove Segmentation Dataset. This dataset provides challenging examples to build applications toward hand and glove segmentation in industrial human-robot collaboration scenarios as well as assess out-of-distribution images, constructed via green screen augmentations, to determine ML-classifier robustness. We study state-of-the-art, real-time segmentation models to evaluate existing methods. Our dataset and baselines are publicly available.