* External authors




Learning Perceptual Hallucination for Multi-Robot Navigation in Narrow Hallways

Jin-Soo Park*

Xuesu Xiao*

Garrett Warnell*

Harel Yedidsion*

Peter Stone

* External authors

ICRA 2023



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.

Related Publications

Symbolic State Space Optimization for Long Horizon Mobile Manipulation Planning.

International Conference on Intelligent Robots and Systems, 2023
Xiaohan Zhang*, Yifeng Zhu*, Yan Ding*, Yuqian Jiang*, Yuke Zhu*, Peter Stone, Shiqi Zhang*

In existing task and motion planning (TAMP) research, it is a common assumption that experts manually specify the state space for task-level planning. A welldeveloped state space enables the desirable distribution of limited computational resources between task planning an…

Event Tables for Efficient Experience Replay

CoLLAs, 2023
Varun Kompella, Thomas Walsh, Samuel Barrett, Peter R. Wurman, Peter Stone

Experience replay (ER) is a crucial component of many deep reinforcement learning (RL) systems. However, uniform sampling from an ER buffer can lead to slow convergence and unstable asymptotic behaviors. This paper introduces Stratified Sampling from Event Tables (SSET), whi…

Composing Efficient, Robust Tests for Policy Selection

UAI, 2023
Dustin Morrill, Thomas Walsh, Daniel Hernandez, Peter R. Wurman, Peter Stone

Modern reinforcement learning systems produce many high-quality policies throughout the learning process. However, to choose which policy to actually deploy in the real world, they must be tested under an intractable number of environmental conditions. We introduce RPOSST, a…

  • HOME
  • Publications
  • Learning Perceptual Hallucination for Multi-Robot Navigation in Narrow Hallways


Shape the Future of AI with Sony AI

We want to hear from those of you who have a strong desire
to shape the future of AI.