Neural Program Synthesis from Diverse Demonstration Videos
Journal
Proceedings of Machine Learning Research
Journal Volume
80
Start Page
4790
End Page
4799
ISSN
26403498
Date Issued
2018
Author(s)
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. The code is available at https://shaohua0116.github.io/demo2program.
Event(s)
35th International Conference on Machine Learning, ICML 2018
Publisher
ML Research Press
Type
conference paper