連続環境での指示誘導ナビゲーションのためのウェイポイントモデル
Waypoint Models for Instruction-guided Navigation in Continuous Environments
言語指示に従う連続環境ナビゲーション(VLN-CE)において、低レベル行動から連続ウェイポイント予測まで表現力を変えたモデルを比較し、実行時間と成功率のトレードオフを分析した上で、公開リーダーボードで成功率を4%向上させる新たな最先端を達成した。
著者: Jacob Krantz, Aaron Gokaslan, Dhruv Batra, Stefan Lee, Oleksandr Maksymets
分類: cs.CV, cs.CL, cs.RO
原文アブストラクト
Little inquiry has explicitly addressed the role of action spaces in language-guided visual navigation -- either in terms of its effect on navigation success or the efficiency with which a robotic agent could execute the resulting trajectory. Building on the recently released VLN-CE setting for instruction following in continuous environments, we develop a class of language-conditioned waypoint prediction networks to examine this question. We vary the expressivity of these models to explore a spectrum between low-level actions and continuous waypoint prediction. We measure task performance and estimated execution time on a profiled LoCoBot robot. We find more expressive models result in simpler, faster to execute trajectories, but lower-level actions can achieve better navigation metrics by approximating shortest paths better. Further, our models outperform prior work in VLN-CE and set a new state-of-the-art on the public leaderboard -- increasing success rate by 4% with our best model on this challenging task.
