Narrate2Nav: 人間中心環境における暗黙的言語推論を用いたリアルタイム視覚ナビゲーション
Narrate2Nav: Real-Time Visual Navigation with Implicit Language Reasoning in Human-Centric Environments
大規模視覚言語モデルの推論を潜在空間に埋め込み、RGB入力から低レベル動作コマンドを生成するリアルタイム視覚-行動モデルを提案。人間中心環境でのナビゲーション性能を大幅に向上させた。
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著者: Amirreza Payandeh, Anuj Pokhrel, Daeun Song, Marcos Zampieri, Xuesu Xiao
分類: cs.RO
原文アブストラクト
Large Vision-Language Models (VLMs) have demonstrated potential in enhancing mobile robot navigation in human-centric environments by understanding contextual cues, human intentions, and social dynamics while exhibiting reasoning capabilities. However, their computational complexity and limited sensitivity to continuous numerical data impede real-time performance and precise motion control. To this end, we propose Narrate2Nav, a novel real-time vision-action model that leverages a novel self-supervised learning framework based on the Barlow Twins redundancy reduction loss to embed implicit natural language reasoning, social cues, and human intentions within a visual encoder-enabling reasoning in the model's latent space rather than token space. The model combines RGB inputs, motion commands, and textual signals of scene context during training to bridge from robot observations to low-level motion commands for short-horizon point-goal navigation during deployment. Extensive evaluation of Narrate2Nav across various challenging scenarios in both offline unseen dataset and real-world experiments demonstrates an overall improvement of 52.94 percent and 41.67 percent, respectively, over the next best baseline. Additionally, qualitative comparative analysis of Narrate2Nav's visual encoder attention map against four other baselines demonstrates enhanced attention to navigation-critical scene elements, underscoring its effectiveness in human-centric navigation tasks.
