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We study the problem of developing autonomous agents that can follow humaninstructions to infer and perform a sequence of actions to complete theunderlying task. Significant progress has been made in recent years, especiallyfor tasks with short horizons. However, when it comes to long-horizon taskswith extended sequences of actions, an agent can easily ignore someinstructions or get stuck in the middle of the long instructions and eventuallyfail the task. To address this challenge, we propose a model-agnosticmilestone-based task tracker (M-TRACK) to guide the agent and monitor itsprogress. Specifically, we propose a milestone builder that tags theinstructions with navigation and interaction milestones which the agent needsto complete step by step, and a milestone checker that systemically checks theagent's progress in its current milestone and determines when to proceed to thenext. On the challenging ALFRED dataset, our M-TRACK leads to a notable 33% and52% relative improvement in unseen success rate over two competitive basemodels.

One Step at a Time: Long-Horizon Vision-and-Language Navigation with Milestones