Student Talks
Carnegie Mellon University
Observing Humans In Their Natural Habitat: Data, Algorithms, and Analysis
Abstract: Computer vision has a great potential to help our daily lives by searching for lost keys, watering flowers or reminding us to take a pill. To succeed with such tasks, computer vision methods need to be trained from real and diverse examples of our daily dynamic scenes. First, we need to give computers insight [...]
Carnegie Mellon University
Ergodic Coverage and Active Search in Constrained Environments
In this thesis, we explore sampling-based trajectory optimization applied to search for objects of interest in constrained environments (e.g., a UAV searching for a target in the presence of obstacles). We consider two search scenarios: in the first scenario, accurate prior information distribution of the possible locations of the objects of interest is available, thus [...]
Carnegie Mellon University
Analysis of Spatio-Temporally Varying Features in Optical Coherence Tomographic (OCT) and Ultrasound (US) Image Sequences
Abstract: Optical Coherence Tomography (OCT) and Ultrasound (US) are non-ionizing and non-invasive imaging modalities that are clinically used to visualize anatomical structures in the body. OCT has been widely adopted in clinical practice due to its micron-scale resolution to visualize in-vivo structures of the eye. Ultra-High Frequency Ultrasound (UHFUS) can capture images at a depth [...]
Carnegie Mellon University
Planning for Energy-Efficient Coverage and Exploratory Deviation by Robots in Rivers
Abstract: Manual collection of environmental data over a large area can be a time-consuming, costly, and even dangerous process, making it a perfect candidate for automation with mobile robots. Despite this clear suitability and numerous advances in robotics resulting in decreased costs, improved reliability, and increased ease of use, the problem of powering autonomous robots [...]
Carnegie Mellon University
Learning to Learn for Small Sample Visual Recognition
Abstract: Understanding how humans and machines recognize novel visual concepts from few examples remains a fundamental challenge. Humans are remarkably able to grasp a new concept and make meaningful generalization from just few examples. By contrast, state-of-the-art machine learning techniques and visual recognition systems typically require thousands of training examples and often break down if [...]
Carnegie Mellon University
Understanding Machine Vision through Human Vision
Abstract: Recent success in machine vision has been largely driven by advanced computer vision methods, most commonly known as deep learning based methods. While we have seen tremendous performance improvements in machine visual tasks, such as object categorization and segmentation, there remain two major issues in deep learning. Firstly, deep networks have been largely unable [...]
Model Predictive Path Following for Wheeled Mobile Robots
Abstract: The navigation success of a wheeled mobile robotic mission is directly correlated to the degree of accuracy to which the robot can follow a given path. This, in turn, is largely affected by two factors: a) the environment and b) the intrinsic properties of the robot – its design, actuation mechanism etc. In the [...]
Carnegie Mellon University
Generative Models of Orbital and In Situ Data for Autonomous Science
Abstract: The mapping and characterization of planetary bodies relies on the analysis of data collected by spacecraft and orbiters. For example, the instruments carried by the Mars Reconnaissance Orbiter have been crucial in the mapping of landforms, stratigraphy, minerals, and ice of Mars. These instruments provide extensive contextual information, but factors such as sparsity, resolution, [...]
Carnegie Mellon University
Automated design, accessible fabrication, and learning-based control on cable-driven soft robots with complex shapes
The emerging field of soft robots has shown great potential to outperform their rigid counterparts due to the soft and safe nature and the capability of performing complex and compliant motions. Many are built, but the designs are conservative and limited to regular shapes. The widely-used fabrication method contains bulky pumps, tethered tubings, and silicone [...]
Carnegie Mellon University
Learning to Forecast Egocentric and Allocentric Behavior in Diverse Domains
Abstract: Reasoning about the future is fundamental to intelligence. In this work, I consider the problem of reasoning about the future actions of an intelligent agent. This poses two key questions. How can we build learning-based systems to forecast the behavior of observed agents (third-person, "allocentric forecasting")? More challenging is the question: how should we [...]