Authors

* External authors

Venue

Date

Share

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

2023

Abstract

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

ProtoCRL: Prototype-based Network for Continual Reinforcement Learning

RLC, 2025
Michela Proietti*, Peter R. Wurman, Peter Stone, Roberto Capobianco

The purpose of continual reinforcement learning is to train an agent on a sequence of tasks such that it learns the ones that appear later in the sequence while retaining theability to perform the tasks that appeared earlier. Experience replay is a popular method used to mak…

Automated Reward Design for Gran Turismo

NeurIPS, 2025
Michel Ma, Takuma Seno, Kaushik Subramanian, Peter R. Wurman, Peter Stone, Craig Sherstan

When designing reinforcement learning (RL) agents, a designer communicates the desired agent behavior through the definition of reward functions - numerical feedback given to the agent as reward or punishment for its actions. However, mapping desired behaviors to reward func…

Proto Successor Measure: Representing the Space of All Possible Solutions of Reinforcement Learning

ICML, 2025
Siddhant Agarwal*, Harshit Sikchi, Peter Stone, Amy Zhang*

Having explored an environment, intelligent agents should be able to transfer their knowledge to most downstream tasks within that environment. Referred to as ``zero-shot learning," this ability remains elusive for general-purpose reinforcement learning algorithms. While rec…

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

JOIN US

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.