Title: Leveraging Simulation for Computer Vision   Abstract: A large amount of labeled data is required to train deep neural networks. The process of data annotation on such a large scale is expensive and time-consuming. A promising alternative in this regard is to use simulation to generate labeled synthetic data. However, a network trained solely [...]

Title: Visual Imitation Learning for Robot Manipulation   Abstract:   Imitation learning has been successfully applied to solve a variety of tasks in complex domains where an explicit reward function is not available. However, most imitation learning methods require access to the robot's actions during demonstration. This stands in a stark contrast to how we [...]

Title: Highly Miniaturized Robots for Inspection of Small Nuclear Piping   Abstract: Bomb making in the 20th century resulted in the creation of massive facilities to produce Uranium. As part of a multi-billion-dollar agenda, the measurement of radioactivity is required for the safe disposal of residual Uranium in piping. Manual techniques have proven too approximate, [...]

Title: Topological Path Planning for Mobile Robot Applications   Abstract: Many path planning problems in mobile robot applications can be solved more efficiently in the topological space. By using the language of topology, the richer spatial information failed to captured by graph/grid-based map representations can be explicitly expressed and exploited. With that, it is possible [...]

Title: Deep Reinforcement Learning with Prior Knowledge   Abstract: Deep reinforcement learning has been applied to many domains from computer games, natural language processing, recommendation systems to robotics.  While model-free reinforcement learning algorithms are promising approaches to learning policies without knowledge of the system dynamics, they usually require much more data. In this thesis, we [...]

Title: 3D Object Detection from CT Scans using a Slice-and-fuse Approach   Abstract: Automatic object detection in 3D X-ray Computed Tomography imagery has recently gained research attention due to its promising applications in aviation baggage screening. The huge dimension of an individual 3D scan, however, poses formidable computational challenges when coupled with deep 3D convolutional [...]

Title: Interpretable Intuitive Physics Model   Abstract: Humans have a remarkable ability to use physical commonsense and predict the effect of collisions. But do they understand the underlying factors? Can they predict if the underlying factors have changed? Interestingly, in most cases humans can predict the effects of similar collisions with different conditions such as changes [...]

Title: Learning for Registration in 2D and 3D   Abstract: We explore the application of deep learning to 2D (image) and 3D (point cloud) registration, especially in scenarios where traditional methods can fail.   In the 2D case, we apply a recently-proposed learning method to the problem of aligning outdoor imagery taken across different seasons or [...]

Title: Inverse Reinforcement Learning for Autonomous Ground Navigation Using Aerial and Satellite Observation Data   Abstract: Inverse Reinforcement Learning(IRL) is a supervised learning paradigm where a learner observes expert demonstrations to learn the hidden cost function to mimic the expert's behavior. Eliminating the need for elaborate feature engineering, deep IRL approaches have been gaining interests [...]