Paper Detail
Hojin Lee, Yunho Lee, Daniel A Duecker, Cheolhyeon Kwon
Traversability prediction is a critical component of autonomous navigation in unstructured environments, where complex and uncertain robot-terrain interactions pose significant challenges such as traction loss and dynamic instability. Despite recent progress in learning-based traversability prediction, these methods often fail to adapt to novel terrains. Even when adaptation is achieved, retaining experience from previously trained environments remains a challenge, a problem known as catastrophic forgetting. To address this challenge, we propose a continual learning framework for traversability prediction that incrementally adapts to new terrains using a generative experience recall model. A key virtue of the proposed framework is two folds: i) retain prior experience without storing past data; and ii) incorporate the uncertainty of the generated samples from the recall model, enabling uncertainty-aware adaptation. Real-world experiments with a skid-steering robot validate the effectiveness of the proposed framework, demonstrating its ability to adapt across a series of diverse environments while mitigating catastrophic forgetting.
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@article{lee2026continual,
title = {Continual Learning for Traversability Prediction with Uncertainty-Aware Adaptation},
author = {Hojin Lee and Yunho Lee and Daniel A Duecker and Cheolhyeon Kwon},
year = {2026},
abstract = {Traversability prediction is a critical component of autonomous navigation in unstructured environments, where complex and uncertain robot-terrain interactions pose significant challenges such as traction loss and dynamic instability. Despite recent progress in learning-based traversability prediction, these methods often fail to adapt to novel terrains. Even when adaptation is achieved, retaining experience from previously trained environments remains a challenge, a problem known as catastrophi},
url = {https://arxiv.org/abs/2609.17141},
keywords = {cs.RO, cs.AI, cs.LG},
eprint = {2609.17141},
archiveprefix = {arXiv},
}
{}