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

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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.