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Recent years have seen the rise of statistical program learning based onneural models as an alternative to traditional rule-based systems forprogramming by example. Rule-based approaches offer correctness guarantees inan unsupervised way as they inherently capture logical rules, while neuralmodels are more realistically scalable to raw, high-dimensional input, andprovide resistance to noisy I/O specifications. We introduce PLANS (ProgramLeArning from Neurally inferred Specifications), a hybrid model for programsynthesis from visual observations that gets the best of both worlds, relyingon (i) a neural architecture trained to extract abstract, high-levelinformation from each raw individual input (ii) a rule-based system using theextracted information as I/O specifications to synthesize a program capturingthe different observations. In order to address the key challenge of makingPLANS resistant to noise in the network's output, we introduce a filteringheuristic for I/O specifications based on selective classification techniques.We obtain state-of-the-art performance at program synthesis from diversedemonstration videos in the Karel and ViZDoom environments, while requiring noground-truth program for training. We make our implementation available atgithub.com/rdang-nhu/PLANS.

PLANS: Robust Program Learning from Neurally Inferred Specifications