CAREER:Designing Robots that Learn: Closing the Gap Between Machine Learning and Engineering

当机器人与环境的互动可以被精确定义时，机器人技术就取得了巨大的成功。然而，如果机器人用来预测、计划和控制其行为的模型不准确，则可能导致次优甚至危险的行为。 不幸的是，随着机器人及其环境变得越来越复杂，准确地指定机器人行为变得越来越困难。 另一种方法是从经验中学习模型，但大多数这种方法需要大量的数据，在各种情况下，这是实际上不可行的收集。这个项目试图结合联合收割机手工制作的，基于物理的模型和机器学习算法的优势，以解决这些问题。 这种综合方法将使工程师能够更好地设计出能够在结构化程度较低的现实环境中运行的机器人。该项目还通过提供将联合收割机机器学习和机器人技术相结合的跨学科经验来实现与这一愿景相关的教育目标。该项目的目标是开发新的理论和算法，以缩小机器学习和机器人工程方法之间的差距，这在传统上是分开研究的。虽然工程使用物理知识来提供可解释性，透明度，并保证工程系统的可靠性和鲁棒性，但机器学习研究数据和信息，并提供专注于模型表达性，计算成本和学习算法的样本效率的保证。 目前的项目包括三个研究举措，使这些学科更紧密地联系在一起。第一个目标是开发新的半参数模型，结合联合收割机参数物理模型和非参数统计模型的机器人。第二个将解决的问题，学习状态空间模型的数据时，不完整的信息动态和状态。第三个奖项将研究如何将工程学中的结构知识用于约束非参数学习算法和深度神经网络模型的假设空间。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

Robotics has enjoyed great success when a robot's interaction with its environment can be precisely defined. However, if the models that robots use to predict, plan, and control their behaviors are inaccurate, it can lead to suboptimal or even dangerous behaviors. Unfortunately, as robots and their environments become more complex, it is increasingly difficult to accurately specify robot behavior. An alternate approach is to learn the models from experience, but most such approaches need large amounts of data, in a variety of situations, which is practically infeasible to collect. This project attempts to combine the strengths of hand-crafted, physics-based models and machine learning algorithms to tackle such problems. Such a combined approach will better position engineers to design robots that can operate in less-structured real-world environments. The project also addresses educational goals related to this vision by providing cross-disciplinary experiences that combine machine learning and robotics. The goal of this project is to develop new theory and algorithms that close the gap between machine learning and engineering approaches to robotics, which have traditionally been studied separately. While engineering uses physics knowledge to provide interpretability, transparency, and guarantees about the reliability and robustness of engineered systems, machine learning studies data and information, and provides guarantees that focus on the expressivity of models, computational cost, and sample efficiency of learning algorithms. The current project consists of three research initiatives to bring these disciplines closer together. The first aims to develop new semi-parametric models for robotics that combine parametric physical models and non-parametric statistical models. The second will tackle the problem of learning state space models from data when given incomplete information about dynamics and state. The third will investigate how structural knowledge from engineering can be used to constrain the hypothesis space of nonparametric learning algorithms and deep neural network models.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.