Factorizing Perception and Policy for Interactive Instruction Following

Kunal Pratap Singh, Suvaansh Bhambri, Byeonghwi Kim, Roozbeh Mottaghi, Jonghyun Choi; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 1888-1897

Abstract

Performing simple household tasks based on language directives is very natural to humans, yet it remains an open challenge for an AI agent. The 'interactive instruction following' task attempts to make progress towards building an agent that can 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. We empirically validate that our model outperforms prior arts by significant margins on the ALFRED benchmark in all metrics with improved generalization.

Related Material

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[bibtex]
@InProceedings{Singh_2021_ICCV, author = {Singh, Kunal Pratap and Bhambri, Suvaansh and Kim, Byeonghwi and Mottaghi, Roozbeh and Choi, Jonghyun}, title = {Factorizing Perception and Policy for Interactive Instruction Following}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2021}, pages = {1888-1897} }