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给生活质量带来革命性的变化。参数建模技术是机器人在这些领域的预测和控制的基础，当机器人与环境的相互作用可以用牛顿物理学精确描述时，参数建模技术得到了巨大的成功。随着复杂的机器人技术进入自然和人类环境，用参数模型先验地描述这些相互作用变得更加困难。因此，机器学习是一个越来越重要的工具：复杂和嘈杂的动力学模型可以直接从机器人与环境的交互中学习。特别是，非参数学习显示出了非凡的前景，当应用于困难的建模问题时，它的表现往往优于参数化的、基于物理的模型。然而，非参数方法也有实际的缺点：它们没有包含基于物理的见解和约束等先验知识，它们是数据密集型的，并且它们通常会产生巨大的计算成本。这是一项关于非参数统计模型如何与基于参数物理的模型更好地集成在机器人预测和控制中的探索性研究。这个项目的重点是开发新的半参数模型，这些模型优雅地组成参数和非参数组件，以实现准确、健壮的建模。

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.