Ann Majewicz is an Assistant Professor of Mechanical Engineering at the University of Texas at Dallas (UTD), and holds a joint appointment in the Department of Surgery at UT Southwestern Medical Center (UTSW), where she joined in August 2014. She directs the Human-Enabled Robotic Technology Laboratory, at both UTD and UTSW, and is a co-director for the Biomedical Innovation program at UTSW. She received her B.S. degrees in Mechanical and Electrical Engineering from the University of St. Thomas in 2008, an M.S. degree from Johns Hopkins in 2011, and a Ph.D.
Bruan Lecture Hall (in the Mudd Chemistry Building, next door to the Braun Auditorium)
Open to the public
Recent years have seen impressive progress in robot control and perception including adept manipulation, aggressive quadrotor maneuvers, dense metric map reconstruction, and object recognition in real time. The grand challenge in robotics today is to capitalize on these advances in order to enable autonomy at a higher-level of intelligence. It is compelling to envision teams of autonomous robots in environmental monitoring, precision agriculture, construction and structure inspection, security and surveillance, and search and rescue.
Bruan Lecture Hall (in the Mudd Chemistry Building, next door to the Braun Auditorium)
Open to the public
Recent technological advances have given way to a new generation of versatile legged robots. These machines are envisioned to replace first responders in disaster scenarios and enable unmanned exploration of distant planets. To achieve these aims, however, our robots must be able to manage physical interaction through contact to move through unstructured terrain. This talk reports on the development of control systems for legged robots to achieve unprecedented levels of dynamic mobility by addressing many critical problems for contact interaction with the environment.
Bruan Lecture Hall (in the Mudd Chemistry Building, next door to the Braun Auditorium)
Open to the public
Given a stream of raw, multi-modal sensory input data, an autonomous robot has to continuously make decisions on how to act for achieving a specific task. This requires the robot to map a very high-dimensional space (sensory data) to another high-dimensional space (motor commands). The non-linear relationship between these can only be captured if we introduce suitable biases and task-specific prior knowledge that structures this mapping. At the same time, these biases have to provide enough flexibility to cope with the expected variability in the robot task.
In robot collectives, interactions between large numbers of individually simple robots lead to complex global behaviors. A great source of inspiration is social insects such as ants and bees, where thousands of individuals coordinate to handle advanced tasks like food supply and nest construction in a remarkably scalable and error tolerant manner. Likewise, robot swarms have the ability to address tasks beyond the reach of single robots, and promise more efficient parallel operation and greater robustness due to redundancy. Key challenges involve both control and physical implementation.
