ODG-NoMaD： overhead相机方向引导的NoMaD

中文摘要 AI 辅助

NoMaD[31]是一种学习型视觉导航策略，它在单一的目标掩码扩散策略中统一了目标条件导航与探索。然而在未见过的环境中，既无目标图像也无拓扑地图可用时，它只能进行无方向探索，在缺乏全局感知的情况下漫无目的地游荡。我们提出ODG-NoMaD，无需重新训练策略，就能为NoMaD的探索模式提供全局前进方向感知。部署时使用一次 overhead深度相机构建占据地图并规划全局路径，将其分段以得到期望航向；机器人机载深度生成的逐帧可通行性地图随后将其细化为无碰撞方向。将余弦方向代价的梯度注入最终去噪步骤，在保留探索多模态性的同时，将采样轨迹旋转至该方向。在存在和不存在随机障碍物的模拟办公环境中，ODG-NoMaD与无引导探索相比，将目标的残余距离减少了多达一个数量级，优于NaviDiffusor[37]的点目标代价引导，且是唯一在所有试验中均保持无碰撞的配置。

英文摘要

NoMaD [31] is a learned vision-navigation policy that unifies goal-conditioned navigation and exploration in a single goal-masked diffusion policy. In an unseen environment, however - where neither a goal image nor a topological map is available - it can only explore undirectedly, wandering without global awareness. We present ODG-NoMaD, which gives NoMaD's exploration mode a global sense of where to proceed, without retraining the policy. An overhead depth camera is used once on deployment to build an occupancy map and plan a global path, which is segmented to yield a desired heading; a per-frame traversability map from the robot's onboard depth then refines this into a collision-free direction. The gradient of a cosine direction cost is injected into the final denoising steps, rotating sampled trajectories toward this direction while preserving the multimodality of exploration. In simulated office environments with and without random obstacles, ODG-NoMaD reduces the residual distance to the target by up to an order of magnitude over unguided exploration, outperforms the point-goal cost guidance of NaviDiffusor [37], and is the only configuration that remains collision-free on every trial.

发表机构

• Applied Research Center for Autonomous Machines, Infosys Center for Emerging Technologies（Infosys新兴技术中心自主机器应用研究中心）
• Dept. of Mechanical Engineering, IIT Kanpur（印度理工学院坎普尔分校机械工程系）

机构由 AI 辅助整理，请以论文原文为准。