PhD Thesis Proposal
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 [...]
Harnessing Task Mechanics for Robotic Manipulation: Modeling, Uncertainty Reduction and Control
Jiaji Zhou Carnegie Mellon University Abstract A high-fidelity and tractable mechanics model of the physical interaction is essential for autonomous robotic manipulation in complex and uncertain environments. Nonetheless, task mechanics are often ignored or nullified in most robotic manipulation systems. This thesis proposal addresses three aspects of harnessing task mechanics: mechanics model learning, uncertainty reduction [...]
Sankalp Arora: Safe, Efficient Data Gathering in Physical Spaces
Sankalp Arora Ph.D. Thesis Proposal Abstract: Reliable and efficient acquisition of data from physical spaces will have countless applications in industry, policy and defense. The capability of gaining information at different scales makes Micro-Aerial Vehicles (MAVs) excellent for aforementioned applications. However, reasoning about information gathering at multiple resolution is NP-Hard and the state of the [...]
Sasanka Nagavalli: Behavior Composition in Human Interaction with Robotic Swarms
Sasanka Nagavalli Ph.D. Thesis Proposal Abstract: Robotic swarms are multi-robot systems whose global behavior emerges from local interactions between individual robots and spatially proximal neighboring robots. Each robot can be programmed with several local control laws that can be activated depending on an operator's choice of global swarm behavior (e.g. flocking, aggregation, formation control, area [...]