Problem
Research questions and friction points this paper is trying to address.
Continual Learning
Traversability Prediction
Catastrophic Forgetting
Uncertainty-Aware Adaptation
Continual Learning for Traversability Prediction with Uncertainty-Aware Adaptation
🤖 AI Summary
为解决无人环境下适应新地形时的灾难性遗忘问题，提出一种基于生成经验回忆模型的持续学习框架，实现无数据存储的经验保留和不确定性感知适应。
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
continual learning
traversability prediction
uncertainty-aware adaptation
generative experience recall model
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