Learning Human Perception Dynamics for Informative Robot Communication

• URL: http://arxiv.org/abs/2502.01857v1
• Date: Mon, 03 Feb 2025 22:08:04 GMT
• Title: Learning Human Perception Dynamics for Informative Robot Communication
• Authors: Shenghui Chen, Ruihan Zhao, Sandeep Chinchali, Ufuk Topcu,
• Abstract summary: CoNav-Maze is a simulated robotics environment where a robot navigates using local perception while a human operator provides guidance based on an inaccurate map.<n>To enable efficient human-robot cooperation, we propose Information Gain Monte Carlo Tree Search (IG-MCTS)<n>Central to IG-MCTS is a neural human perception dynamics model that estimates how humans distill information from robot communications.
• Score: 21.170542003568674
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Human-robot cooperative navigation is challenging in environments with incomplete information. We introduce CoNav-Maze, a simulated robotics environment where a robot navigates using local perception while a human operator provides guidance based on an inaccurate map. The robot can share its camera views to improve the operator's understanding of the environment. To enable efficient human-robot cooperation, we propose Information Gain Monte Carlo Tree Search (IG-MCTS), an online planning algorithm that balances autonomous movement and informative communication. Central to IG-MCTS is a neural human perception dynamics model that estimates how humans distill information from robot communications. We collect a dataset through a crowdsourced mapping task in CoNav-Maze and train this model using a fully convolutional architecture with data augmentation. User studies show that IG-MCTS outperforms teleoperation and instruction-following baselines, achieving comparable task performance with significantly less communication and lower human cognitive load, as evidenced by eye-tracking metrics.

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