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Utilization of latent space to capture a lower-dimensional representation ofa complex dynamics model is explored in this work. The targeted application isof a robotic manipulator executing a complex environment interaction task, inparticular, cutting a wooden object. We train two flavours of VariationalAutoencoders---standard and Vector-Quantised---to learn the latent space whichis then used to infer certain properties of the cutting operation, such aswhether the robot is cutting or not, as well as, material and geometry of theobject being cut. The two VAE models are evaluated with reconstruction,prediction and a combined reconstruction/prediction decoders. The resultsdemonstrate the expressiveness of the latent space for robotic interactioninference and the competitive prediction performance against recurrent neuralnetworks.

Learning the Latent Space of Robot Dynamics for Cutting Interaction Inference