We present FAST NAVIGATOR, a general framework for action decoding, which yields state-of-the-art results on the recent Room-to-Room (R2R) Vision-and-Language navigation challenge of Anderson et. al. (2018). Given a natural language instruction and photo-realistic image views of a previously unseen environment, the agent must navigate from a source to a target location as quickly as possible. While all of current approaches make local action decisions or score entire trajectories with beam search, our framework seamlessly balances local and global signals when exploring the environment. Importantly, this allows us to act greedily, but use global signals to backtrack when necessary. Our FAST framework, applied to existing models, yielded a 17% relative gain over the previous state-of-the-art, an absolute 6% gain on success rate weighted by path length (SPL).

本文介绍一种前沿感知的搜索方法，可以在Room-to-Room视觉与语言导航挑战中实现全球指标的路径搜索和动作决策，相较于现有算法，在Success Rate Weighted by Path Length (SPL)的指标上有了6％的绝对收益和17％的相对性提升。

策略性回溯：视觉语言导航中通过回溯进行自我纠正