Neural Program Synthesis from Diverse Demonstration Videos

Shao-Hua Sun*1

Hyeonwoo Noh*2

Sriram Somasundaram1

Joseph J. Lim1

1University of Southern California
2Pohang University of Science and Technology
*Equal contribution

Paper

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Code

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Bibtex

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Abstract

Interpreting decision making logic in demonstration videos is key to collaborating with and mimicking humans. To empower machines with this ability, we propose a neural program synthesizer that is able to explicitly synthesize underlying programs from behaviorally diverse and visually complicated demonstration videos. We introduce a summarizer module as part of our model to improve the network’s ability to integrate multiple demonstrations varying in behavior. We also employ a multi-task objective to encourage the model to learn meaningful intermediate representations for end-to-end training. We show that our model is able to reliably synthesize underlying programs as well as capture diverse behaviors exhibited in demonstrations.




Model Architecture

A brief overview of our model.

  • Demonstration Encoder receives a demonstration video as input and produces an embedding that cap- tures an agent’s actions and perception.
  • Summarizer Module discovers and summarizes where actions diverge between demonstrations and upon which branching conditions subsequent actions are taken.
  • Program Decoder represents the summarized under- standing of demonstrations as a code sequence.



Paper

@inproceedings{sun2018neural,
  title = {Neural Program Synthesis from Diverse Demonstration Videos},
  author = {Sun, Shao-Hua and Noh, Hyeonwoo and Somasundaram, Sriram and Lim, Joseph},
  booktitle = {Proceedings of the 35th International Conference on Machine Learning},
  year = {2018},
}






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