IG-MCTS: Human-in-the-Loop Cooperative Navigation under Incomplete Information

TL;DR

This paper introduces IG-MCTS, an online planning algorithm for human-in-the-loop cooperative navigation under incomplete information, which optimizes movement and communication to reduce cognitive load and maintain performance.

Contribution

It presents a novel IG-MCTS algorithm combined with a neural human perception model, enabling efficient cooperation and generalization in navigation tasks with incomplete information.

Findings

IG-MCTS reduces communication demands

Lower cognitive load as shown by eye-tracking metrics

Maintains task performance comparable to baselines

Abstract

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.