LEGO-Net is a data-driven transformer-based iterative method for LEarning reGular rearrangement of Objects in messy rooms. It takes an input messy scene and attempts to clean the scene via iterative denoising using a Langevin dynamics-like process.
In this work, leveraging the latest development in machine learning and robot learning, we develop an image-based sorting system for tackling this challenging task.
The results indicate that some occurrences of undermelting, overmelting, and material spatter can be detected that can then be correlated to localized defects, delamination, and layer separation.