Accessible Instruction-Following Agent

TL;DR

This paper presents UVLN, a multilingual instruction-following agent for vision-language navigation that leverages machine translation, large language models, and cross-modal transformers to improve accessibility and performance in diverse languages.

Contribution

The work introduces UVLN, a novel framework combining machine translation, large language models, and cross-modal transformers for cross-lingual vision-language navigation, enhancing accessibility and multilingual capabilities.

Findings

Effective cross-lingual navigation demonstrated on Room Across Room Dataset.

Improved accessibility for non-English and low-resource languages.

Qualitative results show enhanced intractability and user understanding.

Abstract

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.