论文标题
自适应元学习，用于识别漫游者 - 瑟龙动力学
Adaptive Meta-Learning for Identification of Rover-Terrain Dynamics
论文作者
论文摘要
流浪者需要了解地形以计划最大化安全性和效率的轨迹。地形类型分类依赖于人类操作员或基于机器学习的图像分类算法的输入。但是，高水平的地形分类通常不足以防止诸如流浪者意外地陷入砂陷阱之类的事件。在这些情况下，可以利用在线流动站的交互数据来准确预测未来的动态并防止对流动站的进一步损害。本文通过通过贝叶斯回归算法（p-alpaca）在参数中的参数中增强了标称模型仿射，从而提出了一种基于元学习的方法来适应流动站动力学的概率预测。引入了一种正规化计划，以鼓励名义和学识渊博的特征正交性，从而导致对不同地形条件下地形参数的可解释概率估计。
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.
