Data-driven Instruction Augmentation for Language-conditioned Control

Data-driven Instruction Augmentation for Language-conditioned Control (DIAL) is a method that uses pre-trained vision-language models (VLMs) to label offline datasets for language-conditioned policy learning.

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Cite this as

Ted Xiao, Harris Chan, Pierre Sermanet, Ayzaan Wahid, Anthony Brohan, Karol Hausman, Sergey Levine, Jonathan Tompson (2024). Dataset: Data-driven Instruction Augmentation for Language-conditioned Control. https://doi.org/10.57702/08mvytk6

DOI retrieved: December 3, 2024

Additional Info

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Created December 3, 2024
Last update December 3, 2024
Defined In https://doi.org/10.48550/arXiv.2211.11736
Author Ted Xiao
More Authors
Harris Chan
Pierre Sermanet
Ayzaan Wahid
Anthony Brohan
Karol Hausman
Sergey Levine
Jonathan Tompson
Homepage https://instructionaugmentation.github.io