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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Title: Robust Deep Vision-based Control Algorithms with Probabilistic Learning
Committee:
Dr. Theodorou, Advisor
Dr. Vela, Co-Advisor
Dr. Coogan, Chair
Dr. Rehg
Dr. Al-Regib
Abstract: The object of the proposed research is to develop safe and robust vision-based control algorithms for autonomous vehicles. In our robust vision-based imitation learning algorithms, we propose the use of Bayesian Neural Networks (BNNs), which provide both a mean value and an uncertainty estimate as output, to enhance the safety of learned control policies when a test-time input differs significantly from the training set. Furthermore, to quickly detect abnormal uncertain situations in vision-based control, we use Model Predictive Control (MPC) to learn how to focus on important areas of the visual input. This attention-based mechanism allows the system to more rapidly detect unsafe conditions when novel obstacles are present in the navigation environment. Another vision-based control algorithm, the PixelMPC with the Deep Optical Flow dynamics, robustifies the vision-based state estimation of the robot. This novel MPC algorithm allows us to predict both robot's optimal path and the path of a pixel-of-interest in the scene. By controlling a pixel with its learned optical flow dynamics, a robot can have better and stable visual information which results in a robust state estimation followed by robust path planning and control. The proposed algorithm is tested in a photorealistic simulation with a high-speed drone racing task.
