Difference between revisions of "ALFRED"
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| − | <b>ALFRED; Action Learning From Realistic Environments and Directives</b> is a benchmark for learning a mapping from natural language instructions and egocentric vision to sequences of actions for household tasks. It includes long, compositional tasks with non-reversible state changes to shrink the gap between research benchmarks and real-world applications | + | <b>ALFRED; Action Learning From Realistic Environments and Directives</b> is a benchmark for learning a mapping from natural language instructions and egocentric vision to sequences of actions for household tasks. It includes long, compositional tasks with non-reversible state changes to shrink the gap between research benchmarks and real-world applications. a benchmark for learning a mapping from natural language instructions and egocentric vision to sequences of actions for household tasks. It includes long, compositional tasks with non-reversible state changes to shrink the gap between research benchmarks and real-world applications. ALFRED consists of expert demonstrations in interactive visual environments for 25k natural language directives. These directives contain both high-level goals like “Rinse off a mug and place it in the coffee maker” and low-level language instructions like "Walk to the coffee maker on the right." |
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<youtube>-YmHT2fSQDo</youtube> | <youtube>-YmHT2fSQDo</youtube> | ||
<youtube>1XoRLNmXffo</youtube> | <youtube>1XoRLNmXffo</youtube> | ||
Revision as of 09:07, 20 May 2023
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ALFRED; Action Learning From Realistic Environments and Directives is a benchmark for learning a mapping from natural language instructions and egocentric vision to sequences of actions for household tasks. It includes long, compositional tasks with non-reversible state changes to shrink the gap between research benchmarks and real-world applications. a benchmark for learning a mapping from natural language instructions and egocentric vision to sequences of actions for household tasks. It includes long, compositional tasks with non-reversible state changes to shrink the gap between research benchmarks and real-world applications. ALFRED consists of expert demonstrations in interactive visual environments for 25k natural language directives. These directives contain both high-level goals like “Rinse off a mug and place it in the coffee maker” and low-level language instructions like "Walk to the coffee maker on the right."
