# Stay on the Path: Instruction Fidelity in Vision-and-Language Navigation

## TL;DR

This paper introduces new evaluation metrics and a more challenging dataset for vision-and-language navigation, emphasizing instruction fidelity over goal achievement, and demonstrates improved agent performance with fidelity-focused rewards.

## Contribution

It proposes the Coverage weighted by Length Score (CLS) metric and the Room-for-Room (R4R) dataset to better evaluate instruction following in VLN tasks.

## Key findings

- Fidelity-focused rewards lead to better instruction adherence.
- CLS provides a more accurate measure of instruction following.
- Extended paths in R4R challenge agents beyond shortest routes.

## Abstract

Advances in learning and representations have reinvigorated work that connects language to other modalities. A particularly exciting direction is Vision-and-Language Navigation(VLN), in which agents interpret natural language instructions and visual scenes to move through environments and reach goals. Despite recent progress, current research leaves unclear how much of a role language understanding plays in this task, especially because dominant evaluation metrics have focused on goal completion rather than the sequence of actions corresponding to the instructions. Here, we highlight shortcomings of current metrics for the Room-to-Room dataset (Anderson et al.,2018b) and propose a new metric, Coverage weighted by Length Score (CLS). We also show that the existing paths in the dataset are not ideal for evaluating instruction following because they are direct-to-goal shortest paths. We join existing short paths to form more challenging extended paths to create a new data set, Room-for-Room (R4R). Using R4R and CLS, we show that agents that receive rewards for instruction fidelity outperform agents that focus on goal completion.