We present a framework for building interactive, real-time, natural
language-instructable robots in the real world, and we open source related
assets (dataset, environment, benchmark, and policies). Trained with behavioral
cloning on a dataset of hundreds of thousands of language-annotated
trajectories, a produced policy can proficiently execute an order of magnitude
more commands than previous works: specifically we estimate a 93.5% success
rate on a set of 87,000 unique natural language strings specifying raw
end-to-end visuo-linguo-motor skills in the real world. We find that the same
policy is capable of being guided by a human via real-time language to address
a wide range of precise long-horizon rearrangement goals, e.g. "make a smiley
face out of blocks". The dataset we release comprises nearly 600,000
language-labeled trajectories, an order of magnitude larger than prior
available datasets. We hope the demonstrated results and associated assets
enable further advancement of helpful, capable, natural-language-interactable
robots. See videos at this https URL.

该研究提出了一个框架，用于在现实世界中建立交互式、实时的、自然语言指导的机器人，并开源相关资产 (数据集、环境、基准和策略)。