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We release two artificial datasets, Simulated Flying Shapes and SimulatedPlanar Manipulator that allow to test the learning ability of video processingsystems. In particular, the dataset is meant as a tool which allows to easilyassess the sanity of deep neural network models that aim to encode, reconstructor predict video frame sequences. The datasets each consist of 90000 videos.The Simulated Flying Shapes dataset comprises scenes showing two objects ofequal shape (rectangle, triangle and circle) and size in which one objectapproaches its counterpart. The Simulated Planar Manipulator shows a 3-DOFplanar manipulator that executes a pick-and-place task in which it has to placea size-varying circle on a squared platform. Different from other widely useddatasets such as moving MNIST [1], [2], the two presented datasets involvegoal-oriented tasks (e.g. the manipulator grasping an object and placing it ona platform), rather than showing random movements. This makes our datasets moresuitable for testing prediction capabilities and the learning of sophisticatedmotions by a machine learning model. This technical document aims at providingan introduction into the usage of both datasets.

Introducing the Simulated Flying Shapes and Simulated Planar Manipulator Datasets