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arXiv · 2306.05766

Data-Link: High Fidelity Manufacturing Datasets for Model2Real Transfer under Industrial Settings

Sunny Katyara·Mohammad Mujtahid·Court Edmondson
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Abstract

High-fidelity datasets play a pivotal role in imbuing simulators with realism, enabling the benchmarking of various state-of-the-art deep inference models. These models are particularly instrumental in tasks such as semantic segmentation, classification, and localization. This study showcases the efficacy of a customized manufacturing dataset comprising 60 classes in the creation of a high-fidelity digital twin of a robotic manipulation environment. By leveraging the concept of transfer learning, different 6D pose estimation models are trained within the simulated environment using domain randomization and subsequently tested on real-world objects to assess domain adaptation. To ascertain the effectiveness and realism of the created data-set, pose accuracy and mean absolute error (MAE) metrics are reported to quantify the model2real gap.

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Sunny Katyara, Mohammad Mujtahid, Court Edmondson. 2023-06-09. Data-Link: High Fidelity Manufacturing Datasets for Model2Real Transfer under Industrial Settings. https://arxiv.org/abs/2306.05766

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