ALFRED：日常タスクの指示を解釈するためのベンチマーク
ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks
自然言語の指示と視点映像から家庭内タスクの行動系列を学習するベンチマークALFREDを提案し、既存モデルでは性能が低いことを示した。
著者: Mohit Shridhar, Jesse Thomason, Daniel Gordon, Yonatan Bisk, Winson Han, Roozbeh Mottaghi, Luke Zettlemoyer, Dieter Fox
分類: cs.CV, cs.AI, cs.CL, cs.LG, cs.RO
原文アブストラクト
We present ALFRED (Action Learning From Realistic Environments and Directives), a benchmark for learning a mapping from natural language instructions and egocentric vision to sequences of actions for household tasks. ALFRED 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." ALFRED tasks are more complex in terms of sequence length, action space, and language than existing vision-and-language task datasets. We show that a baseline model based on recent embodied vision-and-language tasks performs poorly on ALFRED, suggesting that there is significant room for developing innovative grounded visual language understanding models with this benchmark.
