Humans can collaborate and complete tasks based on visual signals and
instruction from the environment. Training such a robot is difficult especially
due to the understanding of the instruction and the complicated environment.
Previous instruction-following agents are biased to English-centric corpus,
making it unrealizable to be applied to users that use multiple languages or
even low-resource languages. Nevertheless, the instruction-following agents are
pre-trained in a mode that assumes the user can observe the environment, which
limits its accessibility. In this work, we're trying to generalize the success
of instruction-following agents to non-English languages with little corpus
resources, and improve its intractability and accessibility. We introduce UVLN
(Universal Vision-Language Navigation), a novel machine-translation
instructional augmented framework for cross-lingual vision-language navigation,
with a novel composition of state-of-the-art large language model (GPT3) with
the image caption model (BLIP). We first collect a multilanguage
vision-language navigation dataset via machine translation. Then we extend the
standard VLN training objectives to a multilingual setting via a cross-lingual
language encoder. The alignment between different languages is captured through
a shared vision and action context via a cross-modal transformer, which encodes
the inputs of language instruction, visual observation, and action decision
sequences. To improve the intractability, we connect our agent with the large
language model that informs the situation and current state to the user and
also explains the action decisions. Experiments over Room Across Room Dataset
prove the effectiveness of our approach. And the qualitative results show the
promising intractability and accessibility of our instruction-following agent.

UVLN (Universal Vision-Language Navigation) 是一种新颖的增强型机器翻译指令框架，利用大型语言模型（GPT3）和图像标题模型（BLIP）的新颖组合，将传统的指令跟随代理推广到多语言和低资源语言等复杂领域，将不同语言之间的对齐通过跨模态变压器，对语言指令、视觉观察和动作决策序列进行编码、捕获和传递。