ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks
This addresses the gap between research benchmarks and real-world applications for embodied AI, but it is incremental as it builds on existing vision-and-language datasets.
The authors tackled the problem of mapping natural language instructions and egocentric vision to action sequences for household tasks by introducing ALFRED, a benchmark with 25k expert demonstrations, and found that a baseline model performed poorly, indicating room for improvement.
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.