Efficient post-consumer waste recycling is one of the key challenges of modern society, as countries struggle to find sustainable solutions to rapidly rising waste levels and avoid increased soil and sea pollution. The US is one of the leading countries in waste generation by volume but recycles less than 35% of its recyclable waste. Recyclable waste is sorted according to material type (paper, plastic, etc.) in material recovery facilities (MRFs) which still heavily rely on manual sorting. Computer vision solutions are an essential component in automating waste sorting and ultimately solving the pollution problem.In this sixth iteration of the VisDA challenge, we introduce a simulation-to-real (Sim2Real) semantic image segmentation competition for industrial waste sorting. We aim to answer the question: can synthetic data augmentation improve performance on this task and help adapt to changing data distributions? Label-efficient and reliable semantic segmentation is essential for this setting, but differs significantly from existing semantic segmentation datasets: waste objects are typically severely deformed and randomly located, which limits the efficacy of both shape and context priors, and have long tailed distributions and high clutter. Synthetic data augmentation can benefit such applications due to the difficulty in obtaining labels and rare categories. However, new solutions are needed to overcome the large domain gap between simulated and real images. Natural domain shift due to factors such as MRF location, season, machinery in use, etc., also needs to be handled in this application.Competitors will have access to two sources of training data: a novel procedurally generated synthetic waste sorting dataset, SynthWaste, as well as fully-annotated waste sorting data collected from a real material recovery facility. The target test set will be real data from a different MRF.