用语言指令预测遮挡区域，让机器人更快更安全导航
Fast Navigation Through Occluded Spaces via Language-Conditioned Map Prediction
- 根据语言指令和局部传感器数据预测可见区域
- 导航性能提升36%，在多边形环境中验证
- 适合需要快速决策的复杂环境导航任务
在杂乱环境中，运动规划常因遮挡和传感器范围有限而面临安全与速度的权衡。本文研究共驾指令是否能帮助机器人更果断地规划同时保持安全。提出PaceForecaster方法，将共驾指令融入局部规划器：输入为机器人的局部传感器视野（Level-1）和共驾指令，输出包括（i）从Level-1可见的所有区域的预测地图（Level-2），以及（ii）基于指令的子目标。该子目标为规划器提供明确指引，以目标导向方式利用预测环境。将PaceForecaster与Log-MPPI控制器结合，在多边形环境中验证，相较于仅使用局部地图的基线，导航性能提升36%。
原文摘要 · Abstract (English)
In cluttered environments, motion planners often face a trade-off between safety and speed due to uncertainty caused by occlusions and limited sensor range. In this work, we investigate whether co-pilot instructions can help robots plan more decisively while remaining safe. We introduce PaceForecaster, as an approach that incorporates such co-pilot instructions into local planners. PaceForecaster takes the robot's local sensor footprint (Level-1) and the provided co-pilot instructions as input and predicts (i) a forecasted map with all regions visible from Level-1 (Level-2) and (ii) an instruction-conditioned subgoal within Level-2. The subgoal provides the planner with explicit guidance to exploit the forecasted environment in a goal-directed manner. We integrate PaceForecaster with a Log-MPPI controller and demonstrate that using language-conditioned forecasts and goals improves navigation performance by 36% over a local-map-only baseline while in polygonal environments.
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