Human-robot cooperative navigation is challenging under incomplete information. We introduce CoNav-Maze, a simulated environment where a robot navigates with local perception while a human operator provides guidance based on an inaccurate map. The robot can share its onboard camera views to help the operator refine their understanding of the environment. To enable efficient cooperation, we propose Information Gain Monte Carlo Tree Search (IG-MCTS), an online planning algorithm that jointly optimizes autonomous movement and informative communication. IG-MCTS leverages a learned Neural Human Perception Model (NHPM) -- trained on a crowdsourced mapping dataset -- to predict how the human's internal map evolves as new observations are shared. User studies show that IG-MCTS significantly reduces communication demands and yields eye-tracking metrics indicative of lower cognitive load, while maintaining task performance comparable to teleoperation and instruction-following baselines. Finally, we illustrate generalization beyond discrete mazes through a continuous-space waterway navigation setting, in which NHPM benefits from deeper encoder-decoder architectures and IG-MCTS leverages a dynamically constructed Voronoi-partitioned traversability graph.

翻译：在不完全信息条件下实现人机协同导航具有挑战性。我们引入了CoNav-Maze仿真环境，其中机器人依靠局部感知进行导航，而人类操作员则基于不精确的地图提供指引。机器人可以共享其车载摄像头视图，以帮助操作员完善其对环境的理解。为实现高效协同，我们提出了信息增益蒙特卡洛树搜索算法（IG-MCTS），这是一种在线规划算法，能联合优化自主移动和信息性通信。IG-MCTS利用一个经过训练的神经人类感知模型（NHPM）——该模型基于众包地图数据集训练——来预测随着新观测信息的共享，人类内部心理地图将如何演变。用户研究表明，IG-MCTS显著降低了通信需求，并产生了表明认知负荷降低的眼动追踪指标，同时保持了与遥操作和指令跟随基线相当的任务性能。最后，我们通过一个连续空间水道导航场景展示了该方法在离散迷宫之外的泛化能力，在该场景中，NHPM受益于更深的编码器-解码器架构，而IG-MCTS则利用了动态构建的基于Voronoi图划分的可通行性图。