Abstract
Path planning in unknown environments is a crucial yet inherently challenging capability for mobile robots, which primarily encompasses two coupled tasks: autonomous exploration and point-goal navigation. In both cases, the robot must perceive the environment, update its belief, and accurately estimate potential information gain on-the-fly to guide planning. In this work, we propose CogniPlan, a novel path planning framework that leverages multiple plausible layouts predicted by a COnditional GeNerative Inpainting model, mirroring how humans rely on cognitive maps during navigation. These predictions, based on the partially observed map and a set of layout conditioning vectors, enable our planner to reason effectively under uncertainty. We demonstrate strong synergy between generative image-based layout prediction and graph-attention-based path planning, allowing CogniPlan to combine the scalability of graph representations with the fidelity and predictiveness of occupancy maps, yielding notable performance gains in both exploration and navigation. We extensively evaluate CogniPlan on two datasets (hundreds of maps and realistic floor plans), consistently outperforming state-of-the-art planners. We further deploy it in a high-fidelity simulator and on hardware, showcasing its high-quality path planning and real-world applicability.
Abstract (translated)
在未知环境中进行路径规划是移动机器人的一项关键且具有挑战性的能力，主要包含两个相互关联的任务：自主探索和目标点导航。在这两种情况下，机器人都必须感知环境、更新其信念，并实时准确地估计潜在的信息收益以指导规划。 在此研究中，我们提出了一种新的路径规划框架——CogniPlan，该框架利用条件生成式图像修复模型预测出的多个可能布局，这类似于人类在导航过程中依赖认知地图的做法。这些基于部分观察的地图和一组布局约束向量的预测使我们的规划器能够在不确定性条件下有效推理。 本研究展示了生成式图像布局预测与图注意力路径规划之间存在强大的协同作用，使CogniPlan能够结合图表示法的可扩展性以及占用地图的精确度和预见能力，在探索和导航方面实现了显著性能提升。我们对两个数据集（数百张地图和现实场景中的平面图）进行了详尽评估，并且始终优于现有最先进技术。 此外，我们在高保真模拟器和硬件上部署了CogniPlan，展示了其高质量的路径规划能力和在真实世界的适用性。
URL
https://arxiv.org/abs/2508.03027
