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.

本文提出了一种基于元学习的方法，通过将贝叶斯回归算法（P-ALPaCA）与参数仿射的标称模型相结合，增强了探测器动力学的概率预测，并引入了一种正则化方法，以鼓励标称和学习特征的正交性，从而导出可解释的各种地形条件下的地形参数的概率估计。