CRII: RI: Semiparametric Approaches to Learning Robot Dynamics

机器人技术正在从医疗保健到汽车安全等广泛领域彻底改变生活质量。参数化建模技术是机器人预测和控制在这些领域的基础，当机器人与环境的相互作用可以精确地由牛顿物理学表征时，可以获得巨大的成功。随着复杂的机器人技术进入自然和人类环境，它变得越来越难以鲁棒地描述这些相互作用的先验参数模型。因此，机器学习是一个越来越重要的工具：复杂和嘈杂的动态模型可以直接从机器人与环境的交互中学习。特别是，非参数学习已经显示出了非凡的前景，在应用于困难的建模问题时，通常优于参数化的基于物理的模型。 然而，非参数方法也有实际的缺点：它们不包含先验知识，如基于物理的见解和约束，它们是数据密集型的，并且它们通常会产生显着的计算成本。这是一个探索性的调查，如何非参数统计模型可以更好地结合参数物理为基础的模型，机器人的预测和控制。这个项目的重点是开发新的半参数模型，优雅地组成参数和非参数组件的准确，强大的建模。

Robotics is revolutionizing quality of life in a wide range of domains from healthcare to automobile safety. Parametric modeling techniques are fundamental to robotic prediction and control in these domains, employed with great success when a robot's interaction with an environment can be precisely characterized by Newtonian physics. As complex robotic technology moves into natural and human environments, it is becoming more difficult to robustly characterize these interactions a priori with parametric models. As a result, machine learning is an increasingly important tool: models of complicated and noisy dynamics can be directly learned from a robot's interaction with its environment. In particular, nonparametric learning has shown exceptional promise, often outperforming parameterized, physics-based models when applied to difficult modeling problems. However, nonparametric approaches also have practical drawbacks: they do not incorporate prior knowledge such as physics-based insights and constraints, they are data-intensive, and they often incur significant computational cost. This is an exploratory investigation of how nonparametric statistical models can be better integrated with parametric physics-based models for robot prediction and control. The focus of this project is on developing new semiparametric models that elegantly compose parametric and nonparametric components for accurate, robust modeling.