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In this paper, we propose a deep convolutional recurrent neural network thatpredicts action sequences for task and motion planning (TAMP) from an initialscene image. Typical TAMP problems are formalized by combining reasoning on asymbolic, discrete level (e.g. first-order logic) with continuous motionplanning such as nonlinear trajectory optimization. Due to the greatcombinatorial complexity of possible discrete action sequences, a large numberof optimization/motion planning problems have to be solved to find a solution,which limits the scalability of these approaches.  To circumvent this combinatorial complexity, we develop a neural networkwhich, based on an initial image of the scene, directly predicts promisingdiscrete action sequences such that ideally only one motion planning problemhas to be solved to find a solution to the overall TAMP problem. A key aspectis that our method generalizes to scenes with many and varying number ofobjects, although being trained on only two objects at a time. This is possibleby encoding the objects of the scene in images as input to the neural network,instead of a fixed feature vector. Results show runtime improvements of severalmagnitudes. Video: https://youtu.be/i8yyEbbvoEk

Deep Visual Reasoning: Learning to Predict Action Sequences for Task and Motion Planning from an Initial Scene Image