Neural Task Programming: Learning to Generalize Across Hierarchical Tasks

@article{Xu2018NeuralTP,
  title={Neural Task Programming: Learning to Generalize Across Hierarchical Tasks},
  author={Danfei Xu and Suraj Nair and Yuke Zhu and J. Gao and Animesh Garg and Li Fei-Fei and S. Savarese},
  journal={2018 IEEE International Conference on Robotics and Automation (ICRA)},
  year={2018},
  pages={1-8}
}
  • Danfei Xu, Suraj Nair, +4 authors S. Savarese
  • Published 2018
  • Computer Science
  • 2018 IEEE International Conference on Robotics and Automation (ICRA)
  • In this work, we propose a novel robot learning framework called Neural Task Programming (NTP), which bridges the idea of few-shot learning from demonstration and neural program induction. [...] Key Method These specifications are fed to a hierarchical neural program, where bottom-level programs are callable subroutines that interact with the environment. We validate our method in three robot manipulation tasks. NTP achieves strong generalization across sequential tasks that exhibit hierarchal and compositional…Expand Abstract
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