Vision-Language Navigation in Continuous Environments (VLN-CE) requires agents to follow natural-language instructions while navigating in real-world-like environments. Most VLN-CE approach\-es adopt a three-stage framework: a waypoint predictor proposes navigable waypoints, and a navigator selects the best waypoint, with a low-level controller executing the movement to it. However, this decoupled paradigm often leads to unreachable waypoints or inconsistencies between planning and control. In this work, instead of predicting isolated waypoints, we introduce a novel paradigm called Trajectory Waypoint, which grounds each candidate waypoint in an executable trajectory. To realize this, we design a Trajectory Waypoint Predictor formulated as a TSDF-guided diffusion policy, which steers trajectory generation away from obstacles, inherently ensuring the reachability of the predicted waypoints. We further propose a trajectory-enhanced navigator that injects the associated trajectory as additional information for planning, enabling strict consistency between high-level semantic decisions and low-level execution. Extensive experiments on the VLN-CE benchmark show that our Trajectory Waypoint paradigm achieves superior performance over the baselines.

翻译：连续环境下的视觉-语言导航任务要求智能体在类真实环境中遵循自然语言指令进行导航。现有VLN-CE方法多采用三阶段框架：航点预测器生成可导航航点，导航器选择最优航点，底层控制器执行移动指令。然而这种解耦范式常导致航点不可达或规划与控制不一致。本文提出名为"轨迹航点"的新范式，通过将每个候选航点锚定至可执行轨迹，而非预测孤立航点。具体实现上，我们设计基于TSDF引导扩散策略的轨迹航点预测器，通过引导轨迹生成避开障碍物，从本质上保障预测航点的可达性。进一步提出轨迹增强型导航器，将关联轨迹作为规划附加信息，实现高层语义决策与底层执行间的严格一致性。在VLN-CE基准上的大量实验表明，本轨迹航点范式相较基线方法取得更优性能。