Uncertainty Modeling of Learning to Enable Probabilistic Perception

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Modern prediction models (e.g., neural networks) have revolutionized applications ranging from business analysis to robotics. Much of the development has focused on increasing average prediction accuracy via extensive data collections and architectures. However, a key weakness of many of these models is that they simply provide an output, with no sense of the confidence or the accuracy of the result. Prediction accuracy of these models can vary based on the amount and diversity of the training data, the model architecture, and the test environment. For example, visual localization models degrades in poor weather, and object detectors do more poorly in environment in which objects can be obscured. Yet in either example, there is typically no measure of accuracy for the predictions. However, other types of prediction models do include measures of the accuracy in their outputs. A success approach has been shown in the probabilistic perception algorithms, which have been handling uncertain outputs, including outliers, for many years. GPS navigation systems must function in the presence of multi-path errors from buildings, and radar tracking sensors must function even with the return of many false positives due to clutter. Successful perception algorithms using these sensors have been developed because there exists uncertainty models capturing most errors. This project will develop new predictions models that merge deep learning outputs with probabilistic perception/reasoning algorithms to improve the ability of robots to navigate in uncertain environments. This research will develop formal uncertainty models of deep learning outputs in combination with probabilistic perception/reasoning algorithms to create holistic, high-integrity frameworks for key robotics functions such as localization, tracking and forecasting. This project leverages the best of both deep learning (processing large amounts of data quickly and accurately) and perception (reasoning over errors and mistakes). By considering three different classes of learning and perception problems (localization, tracking, forecasting), the work will more easily transition to other application domains such as robots in the home, warehouse, busy hotels/museums/train stations, and warehouses; and aerial and underwater vehicles. The project will make datasets and software available to the community, and research results will be disseminated through publications, conferences, courses, industry-targeted workshops and PI meetings. A comprehensive plan for broadening participation will be implemented including hosting under-represented students, working with high school and under-represented students, and training and mentoring undergraduate and graduate students.This project is supported by the cross-directorate Foundational Research in Robotics program, jointly managed and funded by the Directorates for Engineering (ENG) and Computer and Information Science and Engineering (CISE).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.