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Given a natural language instruction and an input scene, our goal is to traina model to output a manipulation program that can be executed by the robot.Prior approaches for this task possess one of the following limitations: (i)rely on hand-coded symbols for concepts limiting generalization beyond thoseseen during training [1] (ii) infer action sequences from instructions butrequire dense sub-goal supervision [2] or (iii) lack semantics required fordeeper object-centric reasoning inherent in interpreting complex instructions[3]. In contrast, our approach can handle linguistic as well as perceptualvariations, end-to-end trainable and requires no intermediate supervision. Theproposed model uses symbolic reasoning constructs that operate on a latentneural object-centric representation, allowing for deeper reasoning over theinput scene. Central to our approach is a modular structure consisting of ahierarchical instruction parser and an action simulator to learn disentangledaction representations. Our experiments on a simulated environment with a 7-DOFmanipulator, consisting of instructions with varying number of steps and sceneswith different number of objects, demonstrate that our model is robust to suchvariations and significantly outperforms baselines, particularly in thegeneralization settings. The code, dataset and experiment videos are availableat https://nsrmp.github.io

Learning Neuro-symbolic Programs for Language Guided Robot Manipulation