跨越前感知地形：用于越野导航的世界模型
Feeling Terrain Before Crossing: World Models for Off-Road Navigation
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中文总结 AI 辅助
提出Feel-WM，首个以本体感觉为条件并预测机器人物理未来与视觉场景的越野导航世界模型，在真实与仿真中优于仅视觉模型，并成功部署于山间小径。
中文摘要 AI 辅助
导航世界模型通过前瞻进行规划，预测每个候选动作序列所产生的未来，并选择最佳方案，而非直接将观测映射到动作。与城市环境不同，在城市环境中预测场景是一个充分的代理，越野导航取决于机器人与地形的交互，因此预测必须不仅涵盖相机将看到的内容，还要涵盖机器人将感受到的内容。然而，现有的以场景为中心的模型无法预测机器人沿规划轨迹将发生多少打滑、倾斜或晃动。本体感觉直接捕捉这些动态，并且当用作输入时，能改善对物理未来的预测。我们提出了Feel-WM，这是第一个以本体感觉为条件并预测机器人将感受到什么以及相机将看到什么的越野导航世界模型。物理未来采取未来本体感觉状态和失败风险的形式，两者均从机器人自身经验中学习，无需人工标签。规划器将物理未来与场景一起展开，并在可分离的评分中将预测的失败风险与目标相似性进行权衡。在真实越野数据和仿真中的实验表明，Feel-WM在开环规划和闭环粗糙地形导航中，在轮式和腿式平台上均优于仅视觉的导航世界模型。在Husky上部署于山间小径时，Feel-WM进行机载规划，预测前方粗糙地面并绕行，完成了端到端策略失败的路线。
英文摘要
Navigation world models plan by foresight, predicting the future that each candidate action sequence produces and selecting the best, rather than mapping observations to actions directly. Unlike urban settings where a predicted scene is a sufficient proxy, off-road navigation hinges on the robot--terrain interaction, so the prediction must cover not only what the camera will see but what the robot will feel. However, existing scene-focused models do not predict how much the robot will slip, tilt or shake along a planned trajectory. Proprioception captures these dynamics directly and, when used as input, improves the prediction of the physical future. We present Feel-WM, the first off-road navigation world model that conditions on proprioception and predicts what the robot will feel alongside what the camera will see. The physical future takes the form of a future proprioceptive state and a failure risk, both learned from the robot's own experience without human labels. The planner rolls out the physical future alongside the scene and weighs the predicted failure risk against goal similarity in a separable score. Experiments on real off-road data and in simulation demonstrate that Feel-WM outperforms visual-only navigation world models in open-loop planning and closed-loop rough-terrain navigation across wheeled and legged platforms. Deployed on a Husky on mountain trails, Feel-WM plans onboard, predicts rough ground ahead and steers around it, completing courses that an end-to-end policy fails.
发表机构
- Seoul National University（首尔大学）
机构由 AI 辅助整理，请以论文原文为准。
