CoFL-S用预测流场代替离散动作，在Habitat基准上比动作token和动作块方法都强，还能直接跑真实机器人，做语言导航的可以看看。
论文提出CoFL-S，一种低层视觉-语言-动作框架，通过预测机器人局部可见扇区的语言条件流场来生成连续轨迹。为训练这一表示，作者将VLN-CE中的整段指令与动作序列转换为帧级局部监督（含子指令、动作、轨迹和密集流场目标）。评估引入连续时间Habitat基准，隔离低层动作接口并使用共享速度命令控制器进行闭环对比，覆盖不同规划频率。在匹配编码器和训练设置下，CoFL-S在所有规划频率上优于动作token和动作块基线，零样本真实世界部署也进一步展示了其对两者的优势。
CoFL-S: Spatially Queryable Sector Flow Fields for Local Language-Conditioned Navigation
Vision-Language Navigation has increasingly emphasized high-level instruction reasoning, memory, global map construction, and instruction decomposition, while the low-level action representation remains comparatively underexplored. We propose CoFL-S, a low-level vision-language-action framework that predicts a language-conditioned flow field over the robot's local visible sector and generates continuous trajectories by rolling out the predicted field. To train this low-level representation, we convert each VLN-CE episode, originally a whole-episode instruction paired with an action sequence, into frame-level local supervision with aligned sub-instructions and matched action, trajectory, and dense flow-field targets. For evaluation, we introduce a continuous-time Habitat benchmark that isolates low-level action interfaces from instruction decomposition and executes all methods through a shared velocity-command controller, enabling decomposition-independent closed-loop comparison across different planner frequencies rather than fixed discrete forward-and-turn transitions in VLN-CE. Under matched encoders and training settings, CoFL-S consistently outperforms action-token and action-chunk baselines across planner frequencies in the continuous-time Habitat benchmark, and zero-shot real-world closed-loop deployment further shows its advantage over both baselines beyond simulation.