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Video representation learning has recently attracted attention in computervision due to its applications for activity and scene forecasting orvision-based planning and control. Video prediction models often learn a latentrepresentation of video which is encoded from input frames and decoded backinto images. Even when conditioned on actions, purely deep learning basedarchitectures typically lack a physically interpretable latent space. In thisstudy, we use a differentiable physics engine within an action-conditionalvideo representation network to learn a physical latent representation. Wepropose supervised and self-supervised learning methods to train our networkand identify physical properties. The latter uses spatial transformers todecode physical states back into images. The simulation scenarios in ourexperiments comprise pushing, sliding and colliding objects, for which we alsoanalyze the observability of the physical properties. In experiments wedemonstrate that our network can learn to encode images and identify physicalproperties like mass and friction from videos and action sequences in thesimulated scenarios. We evaluate the accuracy of our supervised andself-supervised methods and compare it with a system identification baselinewhich directly learns from state trajectories. We also demonstrate the abilityof our method to predict future video frames from input images and actions.

Learning to Identify Physical Parameters from Video Using Differentiable Physics