While current systems for autonomous robot navigation can produce safe and efficient motion plans in static environments, they usually generate suboptimal behaviors when multiple robots must navigate together in confined spaces. For example, when two robots meet each other in a narrow hallway, they may either turn around to find an alternative route or collide with each other. This paper presents a new approach to navigation that allows two robots to pass each other in a narrow hallway without colliding, stopping, or waiting. Our approach, Perceptual Hallucination for Hallway Passing (PHHP), learns to synthetically generate virtual obstacles (i.e., perceptual hallucination) to facilitate passing in narrow hallways by multiple robots that utilize otherwise standard autonomous navigation systems. Our experiments on physical robots in a variety of hallways show improved performance compared to multiple baselines.

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In Proceedings of the 2023 IEEE International Conference on Robotics and Automation (ICRA 2023), London, England, May 2023.

@inproceedings{ICRA23-Park, title={Learning Perceptual Hallucination for Multi-Robot Navigation in Narrow Hallways}, author={Jinsoo Park and Xuesu Xiao and Garrett Warnell and Harel Yedidsion and Peter Stone}, booktitle={Proceedings of the 2023 IEEE International Conference on Robotics and Automation (ICRA 2023)}, month={May}, address={London, England}, url="http://www.cs.utexas.edu/users/ai-lab?ICRA23-Park", year={2023} }