  To be successful, Vision-and-Language Navigation (VLN) agents must be able to
ground instructions to actions based on their surroundings. In this work, we
develop a methodology to study agent behavior on a skill-specific basis --
examining how well existing agents ground instructions about stopping, turning,
and moving towards specified objects or rooms. Our approach is based on
generating skill-specific interventions and measuring changes in agent
predictions. We present a detailed case study analyzing the behavior of a
recent agent and then compare multiple agents in terms of skill-specific
competency scores. This analysis suggests that biases from training have
lasting effects on agent behavior and that existing models are able to ground
simple referring expressions. Our comparisons between models show that
skill-specific scores correlate with improvements in overall VLN task
performance.
