羅博斯特導航

摘要

大规模部署导航系统需要一种最小化感測器假設、能跨機器人形態泛化且高效訓練的方案。然而，現今最佳系統依賴深度感測器、多攝影機套件或預先建構的地圖，限制了其所支援的硬體並增加部署成本。我們提出Robostral Navigate，這是一個基於此規模化目標打造的80億參數視覺語言模型。該模型僅需單目RGB影像串流——此為機器人平台中最普及的感測器——並透過在當前攝影機視角中指向下一個目標位置來預測航點。由於完全在影像空間而非機器人特定座標系中運作，此策略能自然適應攝影機內參與場景尺度的變化，從而在無需重新校準的情況下部署於輪式、足式與空中機器人。我們在35萬個模擬場景中生成240萬條軌跡，以降低對真實世界資料收集的依賴並易於擴展。我們進一步引入一種前綴緩存訓練方案，將整個情節打包成單一訓練序列，使訓練令牌減少22倍，並將訓練時間從數月縮短至數日。基於樹狀結構的注意力遮罩可防止模型依賴先前的真實動作進行條件化，從而鼓勵基於視覺的動作預測；強化學習則用於進一步改善探索與復原能力。在連續環境中的室內到室內（R2R-CE）與跨室內環境（RxR-CE）基準測試中，Robostral Navigate創下新里程碑。在R2R-CE上，儘管僅使用單一RGB攝影機，其仍達到77.4%的成功率，超越最佳單目方法10.5個百分點，並領先最強的深度或多攝影機系統5.3個百分點。在RxR-CE上，其達到75.1%的成功率，優於所有單目基準模型。

English

Deploying navigation systems at scale requires a recipe that minimizes sensor assumptions, generalizes across robot embodiments, and trains efficiently. Yet, today's best systems depend on depth sensors, multi-camera rigs, or pre-built maps, limiting the hardware they support and increasing deployment cost. We introduce Robostral Navigate, an 8B vision-language model built around this scalability objective. The model consumes only a stream of monocular RGB images - the most ubiquitous sensor across robotic platforms and predicts waypoints by pointing to the next target location in the current camera view. Operating purely in image space, rather than robot-specific coordinates, makes the policy naturally robust to changes in camera intrinsics and scene scale, enabling deployment across wheeled, legged, and aerial robots without recalibration. We generate 2.4 million trajectories across 350k simulated scenes to reduce the reliance on real-world data collection and scale easily. We further introduce a prefix-caching training recipe that packs entire episodes into single training sequences, reducing training tokens by 22x and cutting training time from months to days. A tree-based attention mask prevents conditioning on previous ground-truth actions, encouraging visually grounded action prediction, and reinforcement learning is used to further improve exploration and recovery capabilities. On the Room-to-Room and Room-Across-Room in Continuous Environments (R2R-CE and RxR-CE) benchmarks, Robostral Navigate sets a new state of the art. On R2R-CE, it achieves a 77.4% success rate, surpassing the best monocular method by 10.5 points and the strongest depth- or multi-camera system by 5.3 points despite using only a single RGB camera. On RxR-CE, it reaches 75.1% success rate, outperforming all monocular baselines.