Student Talks
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 [...]
Improving Prediction of Traversability for Planetary Rovers Using Thermal Imaging
Event Location: GHC 4405Abstract: The most significant mobility challenges that planetary rovers encounter are compounded by loose, granular materials that cause slippage and sinkage on slopes or are deep enough to entrap a vehicle. The inability of current technology to detect loose terrain hazards has caused significant delays for rovers on both the Moon and [...]
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 [...]
Safe, Efficient, and Robust Predictive Control of Constrained Nonlinear Systems
Vishnu R. Desaraju Carnegie Mellon University April 12, 2017, 2:00 p.m., NSH 1305 Abstract As autonomous systems are deployed in increasingly complex and uncertain environments, safe, accurate, and robust feedback control techniques are required to ensure reliable operation. Accurate trajectory tracking is essential to complete a variety of tasks, but this may be difficult if [...]
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 [...]
Seungmoon Song: The Development, Evaluation and Applications of a Neuromechanical Control Model of Human Locomotion
Seungmoon Song Ph.D. Thesis Defense Abstract: The neural control of human locomotion is not fully understood. As current experimental techniques provide only partial and indirect access to the neural control network, our understanding remains fragmentary with large gaps between detectable neural circuits and measurable behavioral data. Neuromechanical simulation studies can help bridging these gaps. By [...]
Juan Pablo Mendoza: Regions of Inaccurate Modeling for Robot Anomaly Detection and Model Correction
Juan Pablo Mendoza Ph.D. Thesis Defense Abstract: To make intelligent decisions, robots often use models of the stochastic effects of their actions on the world. Unfortunately, in complex environments, it is often infeasible to create models that are accurate in every plausible situation, which can lead to suboptimal performance. This thesis enables robots to reason [...]