ALFRED ![](images/alfred.svg)
A Benchmark for Interpreting Grounded Instructions for Everyday Tasks
Mohit Shridhar | Jesse Thomason | Daniel Gordon | Yonatan Bisk |
Winson Han | Roozbeh Mottaghi | Luke Zettlemoyer | Dieter Fox |
ALFRED (Action Learning From Realistic Environments and Directives), is a new benchmark for learning a mapping from natural language instructions and egocentric vision to sequences of actions for household tasks. Long composition rollouts with non-reversible state changes are among the phenomena we include to shrink the gap between research benchmarks and real-world applications.
Paper on ArXiv» GitHub» Simulator» Leaderboard»
@inproceedings{ALFRED20,
title ={{ALFRED: A Benchmark for Interpreting Grounded
Instructions for Everyday Tasks}},
author={Mohit Shridhar and Jesse Thomason and
Daniel Gordon and Yonatan Bisk and
Winson Han and Roozbeh Mottaghi and
Luke Zettlemoyer and Dieter Fox},
booktitle = {The IEEE Conference on Computer Vision
and Pattern Recognition (CVPR)},
year = {2020},
url = {https://arxiv.org/abs/1912.01734}
}
Data Explorer
Could not parse the specified example name.
|