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Dyna-LfLH: Learning Agile Navigation in Dynamic Environments from Learned Hallucination.
Saad Abdul Ghani, Zizhao
Wang, Peter Stone, and and Xuesu
Xiao.
In International Conference on Intelligent Robots and Systems, October 2025.
This paper introduces Dynamic Learning from Learned Hallucination (Dyna-LfLH), a self-supervised method for training motion planners to navigate environments with dense and dynamic obstacles. Classical planners struggle with dense, unpredictable obstacles due to limited computation, while learning-based planners face challenges in acquiring high-quality demonstrations for imitation learning or dealing with exploration inefficiencies in reinforcement learning. Building on Learning from Hallucination (LfH), which synthesizes training data from past successful navigation experiences in simpler environments, Dyna-LfLH incorporates dynamic obstacles by generating them through a learned latent distribution. This enables efficient and safe motion planner training. We evaluate Dyna-LfLH on a ground robot in both simulated and real environments, achieving up to a 25 percent improvement in success rate compared to baselines.
@InProceedings{dyna_lflh_icra_2025,
author = {Saad Abdul Ghani and Zizhao Wang and Peter Stone and and Xuesu Xiao},
title = {Dyna-LfLH: Learning Agile Navigation in Dynamic Environments from Learned Hallucination},
booktitle = {International Conference on Intelligent Robots and Systems},
year = {2025},
month = {October},
location = {Hangzhou, CHINA},
abstract = {This paper introduces Dynamic Learning from Learned Hallucination (Dyna-LfLH), a self-supervised method for training motion planners to navigate environments with dense and dynamic obstacles. Classical planners struggle with dense, unpredictable obstacles due to limited computation, while learning-based planners face challenges in acquiring high-quality demonstrations for imitation learning or dealing with exploration inefficiencies in reinforcement learning. Building on Learning from Hallucination (LfH), which synthesizes training data from past successful navigation experiences in simpler environments, Dyna-LfLH incorporates dynamic obstacles by generating them through a learned latent distribution. This enables efficient and safe motion planner training. We evaluate Dyna-LfLH on a ground robot in both simulated and real environments, achieving up to a 25 percent improvement in success rate compared to baselines.},
}
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