Sub-Instruction Aware Vision-and-Language Navigation
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Vision-and-language navigation requires an agent to navigate through a real 3D environment following a given natural language instruction. Despite significant advances, few previous works are able to fully utilize the strong correspondence between the visual and textual sequences. Meanwhile, due to the lack of intermediate supervision, the agent's performance at following each part of the instruction remains untrackable during navigation. In this work, we focus on the granularity of the visual and language sequences as well as the trackability of agents through the completion of instruction. We provide agents with fine-grained annotations during training and find that they are able to follow the instruction better and have a higher chance of reaching the target at test time. We enrich the previous dataset with sub-instructions and their corresponding paths. To make use of this data, we propose an effective sub-instruction attention and shifting modules that attend and select a single sub-instruction at each time-step. We implement our sub-instruction modules in four state-of-the-art agents, compare with their baseline model, and show that our proposed method improves the performance of all four agents.
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