Rovers require knowledge of terrain to plan trajectories that maximize safety and efficiency. Terrain type classification relies on input from human operators or machine learning-based image classification algorithms. However, high level terrain classification is typically not sufficient to prevent incidents such as rovers becoming unexpectedly stuck in a sand trap; in these situations, online rover-terrain interaction data can be leveraged to accurately predict future dynamics and prevent further damage to the rover. This paper presents a meta-learning-based approach to adapt probabilistic predictions of rover dynamics by augmenting a nominal model affine in parameters with a Bayesian regression algorithm (P-ALPaCA). A regularization scheme is introduced to encourage orthogonality of nominal and learned features, leading to interpretable probabilistic estimates of terrain parameters in varying terrain conditions.

中文翻译：

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用于识别流动站地形动力学的自适应元学习

漫游车需要了解地形才能规划出最大程度提高安全性和效率的轨迹。地形类型分类依赖于来自人类操作员或基于机器学习的图像分类算法的输入。然而，高级地形分类通常不足以防止诸如漫游车意外陷入沙坑之类的事故。在这些情况下，可以利用在线漫游车-地形交互数据来准确预测未来的动态并防止对漫游车造成进一步的损坏。本文提出了一种基于元学习的方法，通过使用贝叶斯回归算法 (P-ALPaCA) 增强参数中的标称模型仿射来适应漫游车动力学的概率预测。引入正则化方案以鼓励名义特征和学习特征的正交性，