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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 Planning and Control Algorithms with Probabilistic Learning
Committee:
Dr. Evangelos Theodorou, AE, Chair, Advisor
Dr. Patricio Vela, ECE, Co-Advisor
Dr. Ghassan AlRegib, ECE
Dr. Samuel Coogan, ECE
Dr. James Rehg, CoC
Dr. Kyriakos Vamvoudakis, AE
Abstract: Decision making for safety-critical systems is challenging due to performance requirements with significant consequences in the event of failure. Data-driven planning and control methods, e.g. using deep neural networks, are generally not used for safety-critical systems as they can behave in unexpected ways in response to novel or corrupted inputs. This thesis studies how to safely deploy deep learning-based path planning and control algorithms to safety-critical systems (e.g. autonomous cars and drones). This thesis investigates the robot learning from demonstration paradigm, mainly imitation learning and inverse reinforcement learning. A novel system identification approach is also proposed to learn the dynamics of the optical flow. By combining computer vision, probabilistic deep learning, and model predictive control, we can 1) detect uncertain situations and stop the system before it fails, 2) quantify the uncertainty of the deep neural network model's prediction to plan a safer path for the system, and 3) optimize a trajectory to achieve more visual information and more robust state estimation. The proposed algorithms are tested in challenging environments including offroad racing tracks and a simulated dense traffic highway for autonomous driving and a photorealistic sensor simulated environment for drone racing.
