Student Talks
Online Lidar and Vision based Ego-motion Estimation and Mapping
Ji Zhang Carnegie Mellon University Abstract In many real-world applications, ego-motion estimation and mapping must be conducted online. In the robotics world, especially, real-time motion estimates are important for control of autonomous vehicles, while online generated maps are crucial for obstacle avoidance and path planning. Further, the complete map of a traversed environment can be [...]
Creative Robotic Systems for Talent-based Learning
Event Location: GHC 4405Abstract: In recent years, the U.S. educational system has fallen short in training the technology innovators of the future. To do so, we must give students the experience of designing and creating technological artifacts, rather than relegating students to the role of technology consumers, and must provide educators with opportunities and professional [...]
The theory, implementation, and evaluation of spring mass running on ATRIAS, a bipedal robot
Event Location: NSH 3305Abstract: We expect legged robots to be highly mobile. Human walking and running can execute quick changes in speed and direction, even on non-flat ground. Indeed, analysis of simplified models shows that these quantities can be tightly controlled by adjusting the leg placement between steps, and that leg placement can also compensate [...]
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 [...]