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Pushing is a fundamental robotic skill. Existing work has shown how toexploit models of pushing to achieve a variety of tasks, including graspingunder uncertainty, in-hand manipulation and clearing clutter. Such models,however, are approximate, which limits their applicability. Learning-basedmethods can reason directly from raw sensory data with accuracy, and have thepotential to generalize to a wider diversity of scenarios. However, developingand testing such methods requires rich-enough datasets. In this paper weintroduce Omnipush, a dataset with high variety of planar pushing behavior. Inparticular, we provide 250 pushes for each of 250 objects, all recorded withRGB-D and a high precision tracking system. The objects are constructed so asto systematically explore key factors that affect pushing -- the shape of theobject and its mass distribution -- which have not been broadly explored inprevious datasets, and allow to study generalization in model learning.Omnipush includes a benchmark for meta-learning dynamic models, which requiresalgorithms that make good predictions and estimate their own uncertainty. Wealso provide an RGB video prediction benchmark and propose other relevant tasksthat can be suited with this dataset.  Data and code are available at\url{https://web.mit.edu/mcube/omnipush-dataset/}.

Omnipush: accurate, diverse, real-world dataset of pushing dynamics with RGB-D video