PhD Thesis Proposal
Training Strategies for Time Series: Learning for Filtering and Reinforcement Learning
Event Location: GHC 4405Abstract: Data driven approaches to modeling time-series are important in a variety of applications from market prediction in economics to the simulation of robotic systems. However, traditional supervised machine learning techniques designed for i.i.d. data often perform poorly on these sequential problems. This thesis proposes that time series and sequential prediction, whether [...]
Deliberative Perception
Event Location: NSH 1507Abstract: A recurrent and elementary machine perception task is to localize objects of interest in the physical world, be it objects on a warehouse shelf or cars on a road. In many real-world examples, this task entails localizing specific object instances with known 3D models. For example, a warehouse robot equipped with [...]
Designing Data Visualization and Crowdsourcing Systems in Community-based Citizen Science
Event Location: GHC 4405Abstract: Citizen science forges partnerships between experts and citizens through collaboration and has become a trend in public participation in scientific research over the past decade. While public participation has been applied to science education, researchers recently noticed that this strategy can contribute to participatory democracy, which empowers citizens to advocate for [...]
Discovering and Leveraging Visual Structure for Large-scale Recognition
Event Location: GHC 4405Abstract: Visual Recognition has seen tremendous advances in the last decade. This progress is primarily due to learning algorithms trained with two key ingredients: large amounts of data and extensive supervision. While acquiring visual data is cheap, getting it labeled is far more expensive. So how do we enable learning algorithms to [...]
Adaptive Motion Planning
Event Location: NSH 1305Abstract: Mobile robots are increasingly being deployed in the real world in response to a heightened demand for applications such as transportation, delivery and inspection. The motion planning systems for these robots are expected to have consistent performance across the wide range of scenarios that they encounter. While state-of-the art planners can [...]
Robust and Natural Gait via Neuromuscular Control for Transfemoral Prostheses
Nitish Thatte Carnegie Mellon University February 03, 2017, Robust and Natural Gait via Neuromuscular Control for Transfemoral Prostheses, Porter Hall A19C Abstract We present work towards developing a control method for powered knee and ankle prostheses based on a neuromuscular model of human locomotion. Previous research applying neuromuscular control to simulated biped models and to [...]
Data-Driven Visual Forecasting
Event Location: GHC 4405Abstract: Understanding the temporal dimension of images is a fundamental part of computer vision. Humans are able to interpret how the entities in an image will change over time. However, it has only been relatively recently that researchers have focused on visual forecasting—getting machines to anticipate events in the visual world before [...]
Flexible and High-Fidelity Off-Road Lidar Scene Simulation
Event Location: NSH 3305Abstract: As the target scale of robot operations grows, so too does the challenge of developing software for such systems. It may be difficult, unsafe, or expensive to develop software on enough real-world conditions. Similarly, as the target applications of learning algorithms grow, so too do the challenges of gathering adequate training [...]
Extensions of the Principal Fiber Bundle Model for Locomoting Robots
Event Location: NSH 1507Abstract: Our goal is to establish a rigorous formulation for modeling the locomotion of a broad class of robotic systems. Recent research has identified a number of systems with the structure of a principal fiber bundle. This framework has led to a number of tools for analysis and motion planning applicable to [...]
Learning to Learn and Structure Learning in Model Spaces for Small Sample Visual Recognition
Yuxiong Wang Carnegie Mellon University Abstract Understanding how to recognize novel categories from few examples for both humans and machines remains a fundamental challenge. Humans are remarkably able to grasp a new category and make meaningful generalization to novel instances from just few examples. By contrast, state-of-the-art machine learning techniques and visual recognition systems typically [...]