Action Learning From Realistic Environments and Directives (ALFRED) Dataset
Created by Shridhar et al. at 2020, the Action Learning From Realistic Environments and Directives (ALFRED) Dataset contains 8k+ expert demostrations with 3 or more language annotations each comprising of 25,000 language directives. A trajectory consists of a sequence of expert actions, the corresponding image observations, and language annotations describing segments of the trajectory., in English language. Containing 8,055 in JSON file format.
Dataset Sources
Here you can download the Action Learning From Realistic Environments and Directives (ALFRED) dataset in JSON format.
Download Action Learning From Realistic Environments and Directives (ALFRED) dataset JSON files
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Paper
Read full original Action Learning From Realistic Environments and Directives (ALFRED) paper.
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