Continual Learning for Traversability Prediction with Uncertainty-Aware Adaptation

arXiv:2609.17141v1 Announce Type: cross 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 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.

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 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.

Accepted version of the article published in IEEE Robotics and Automation Letters. DOI: https://doi.org/10.1109/LRA.2025.3619687

Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2609.17141 [cs.RO]

(or arXiv:2609.17141v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.17141 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

IEEE Robotics and Automation Letters, vol. 10, no. 11, pp. 12109-12116, Nov. 2025

https://doi.org/10.1109/LRA.2025.3619687 Focus to learn more DOI(s) linking to related resources

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Continual Learning for Traversability Prediction with Uncertainty-Aware Adaptation

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