General Evaluation for Instruction Conditioned Navigation using Dynamic Time Warping

TL;DR

This paper introduces normalized Dynamic Time Warping (nDTW) and Success-constrained DTW (SDTW) as improved evaluation metrics for instruction-conditioned navigation, correlating better with human judgments and enhancing RL agent performance.

Contribution

It proposes new DTW-based metrics for navigation evaluation that address flaws in existing metrics and demonstrates their effectiveness through experiments.

Findings

nDTW correlates better with human judgments than existing metrics.

Using nDTW as a reward improves RL navigation performance.

SDTW outperforms previous success-constrained metrics on R4R dataset.

Abstract

In instruction conditioned navigation, agents interpret natural language and their surroundings to navigate through an environment. Datasets for studying this task typically contain pairs of these instructions and reference trajectories. Yet, most evaluation metrics used thus far fail to properly account for the latter, relying instead on insufficient similarity comparisons. We address fundamental flaws in previously used metrics and show how Dynamic Time Warping (DTW), a long known method of measuring similarity between two time series, can be used for evaluation of navigation agents. For such, we define the normalized Dynamic Time Warping (nDTW) metric, that softly penalizes deviations from the reference path, is naturally sensitive to the order of the nodes composing each path, is suited for both continuous and graph-based evaluations, and can be efficiently calculated. Further, we define SDTW, which constrains nDTW to only successful paths. We collect human similarity judgments for simulated paths and find nDTW correlates better with human rankings than all other metrics. We also demonstrate that using nDTW as a reward signal for Reinforcement Learning navigation agents improves their performance on both the Room-to-Room (R2R) and Room-for-Room (R4R) datasets. The R4R results in particular highlight the superiority of SDTW over previous success-constrained metrics.