IG-MCTS：不完全情報下での人間参加型協調ナビゲーション
IG-MCTS: Human-in-the-Loop Cooperative Navigation under Incomplete Information
ロボットが局所知覚で移動し人間が不正確な地図で指示する協調ナビゲーションにおいて、情報共有と移動を同時最適化するオンライン計画法IG-MCTSを提案し、通信量と認知負荷を低減しつつ性能を維持できることを示した。
著者: Shenghui Chen, Ruihan Zhao, Sandeep Chinchali, Ufuk Topcu
分類: cs.RO, cs.AI
原文アブストラクト
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.
