General Evaluation for Instruction Conditioned Navigation using Dynamic Time Warping

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.

References in corpus (3)

• Matterport3D: Learning from RGB-D Data in Indoor Environments
• Finding Anomalous Periodic Time Series: An Application to Catalogs of Periodic Variable Stars
• From Language to Goals: Inverse Reinforcement Learning for Vision-Based Instruction Following

Cited by in corpus (7)

• Deep Learning for Embodied Vision Navigation: A Survey
• Object-and-Action Aware Model for Visual Language Navigation
• SOON: Scenario Oriented Object Navigation with Graph-based Exploration
• SASRA: Semantically-aware Spatio-temporal Reasoning Agent for Vision-and-Language Navigation in Continuous Environments
• Interactive Learning from Activity Description
• Multimodal Text Style Transfer for Outdoor Vision-and-Language Navigation
• Hierarchical Cross-Modal Agent for Robotics Vision-and-Language Navigation