Factorizing Perception and Policy for Interactive Instruction Following

@article{Singh2021FactorizingPA,
  title={Factorizing Perception and Policy for Interactive Instruction Following},
  author={Kunal Pratap Singh and Suvaansh Bhambri and Byeonghwi Kim and Roozbeh Mottaghi and Jonghyun Choi},
  journal={2021 IEEE/CVF International Conference on Computer Vision (ICCV)},
  year={2021},
  pages={1868-1877}
}
Performing simple household tasks based on language directives is very natural to humans, yet it remains an open challenge for AI agents. The ‘interactive instruction following’ task attempts to make progress towards building agents that jointly navigate, interact, and reason in the environment at every step. To address the multifaceted problem, we propose a model that factorizes the task into interactive perception and action policy streams with enhanced components and name it as MOCA, a… 
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