PhD Speaking Qualifier

Abstract: Reconstruction tasks in computer vision aim fundamentally to recover an undetermined signal from a set of noisy measurements. Examples include super-resolution, image denoising, and non-rigid structure from motion\cite{Kong_2019}, all of which have seen recent advancements through deep learning. However, earlier work made extensive use of sparse signal reconstruction frameworks (e.g. convolutional sparse coding). While [...]

Abstract: There has been great progress in soft robot design, manufacture, and control in recent years, and soft robots are a tool of choice for safe and robust handling of objects in conditions of uncertainty. Still, dexterous in-hand manipulation using soft robots remains a challenge. This talk introduces a novel class of soft robots in [...]

Abstract: Deep reinforcement learning has achieved many successes over the recent years. However, its high sample complexity and the difficulty in specifying a reward function have limited its application. In this talk, I will take a representation learning perspective towards these issues. Is it possible to map from the raw observation, potentially in high dimension, [...]

Zoom Link Abstract Real-world applications usually require computer vision algorithms to meet certain resource constraints. In this talk, I will present evaluation methods and principled solutions for both cases of training and testing. First, I will talk about a formal setting for studying training under the non-asymptotic, resource-constrained regime, i.e., budgeted training. We analyze the [...]

Zoom Link Abstract: Models used in modern planning problems to simulate outcomes of real world action executions are becoming increasingly complex, ranging from simulators that do physics-based reasoning to precomputed analytical motion primitives. However, robots operating in the real world often face situations not modeled by these models before execution. This imperfect modeling can lead [...]

Zoom Link Abstract: As mobile robots are being designed for increasingly rugged and unknown terrain, mechanical reconfigurability presents one possibility for improving vehicle efficiency and mobility. To validate this idea, we created an 18.5-kg modular tracked vehicle with adjustable track tension, track width, track length, and sprocket diameter. In this talk, I will explain the [...]

Zoom Link Abstract: One of the hallmarks of human intelligence is the ability to compose learned knowledge into novel concepts which can be recognized without a single training example. In contrast, current state-of-the-art methods require hundreds of training examples for each possible category to build reliable and accurate classifiers. To alleviate this striking difference in [...]

Zoom Link Abstract: Accurate localization is essential for autonomous operation in many problem domains. This is most often performed by comparing LiDAR scans collected in real-time to a HD point cloud based map. While this enables centimeter-level accuracy, it depends on an expensive LiDAR sensor at run time. Recently, efforts have been underway to reduce [...]

Zoom Link Abstract: Traditionally, computer vision systems and algorithms, such as stereo vision, and shape from shading, have been developed to mimic human vision. As a consequence, a lot of these systems operate under constraints that we take for granted in human vision. An example of such a constraint is that the scene of interest [...]

Zoom Link Abstract: Heuristic search-based motion planning can be computationally costly in large state and action spaces. In this work we explore the use of generative models to learn contextual actions for successor generation in heuristic search. We focus on cases where the robot operates in similar environments, i.e. environments drawn from some underlying distribution. [...]