论文标题
刚体动态的结构化学习：从机器人的角度来看的调查和统一观点
Structured learning of rigid-body dynamics: A survey and unified view from a robotics perspective
论文作者
论文摘要
机械系统动力学的准确模型通常对于基于模型的控制和增强学习至关重要。完全数据驱动的动力学模型有望简化建模和分析的过程，但需要大量的数据进行培训，并且通常不会很好地概括以看不见状态空间的一部分。将数据驱动的建模与先前的分析知识相结合是一种有吸引力的替代方法，因为将结构知识纳入回归模型可以提高模型的数据效率和物理完整性。在本文中，我们调查了将刚性力学与数据驱动建模技术相结合的监督回归模型。我们分析了不同的潜在功能（例如动能或耗散力）和算子（例如差异算子和投影矩阵）的基础刚性机械师的共同描述。基于此分析，我们提供了有关数据驱动的回归模型（例如神经网络和高斯过程）与分析模型先验的组合的统一观点。此外，我们审查并讨论设计用于设计结构化模型（例如自动分化）的关键技术。
Accurate models of mechanical system dynamics are often critical for model-based control and reinforcement learning. Fully data-driven dynamics models promise to ease the process of modeling and analysis, but require considerable amounts of data for training and often do not generalize well to unseen parts of the state space. Combining data-driven modelling with prior analytical knowledge is an attractive alternative as the inclusion of structural knowledge into a regression model improves the model's data efficiency and physical integrity. In this article, we survey supervised regression models that combine rigid-body mechanics with data-driven modelling techniques. We analyze the different latent functions (such as kinetic energy or dissipative forces) and operators (such as differential operators and projection matrices) underlying common descriptions of rigid-body mechanics. Based on this analysis, we provide a unified view on the combination of data-driven regression models, such as neural networks and Gaussian processes, with analytical model priors. Further, we review and discuss key techniques for designing structured models such as automatic differentiation.
