Learning from Limited Data for Robot Vision in the Wild
Speaker: Assistant Professor Dr. Katherine (Katie) Skinner, University of Michigan
Time: 2:40 p.m.-3:30 p.m.
Abstract: Field robotics refers to the deployment of robots and autonomous systems in unstructured or dynamic environments across air, land, sea, and space. Robust sensing and perception can enable these systems to perform tasks such as long-term environmental monitoring, mapping of unexplored terrain, and safe operation in remote or hazardous environments. In recent years, deep learning has led to impressive advances in robot perception. However, state-of-the-art methods still rely on gathering large datasets with hand-annotated labels for network training. For many applications across field robotics, dynamic environmental conditions or operational challenges hinder efforts to collect and manually label large training sets that are representative of all possible environmental conditions a robot might encounter. This limits the performance and generalizability of existing learning-based approaches for robot vision in field applications.
In this talk, I will discuss unique challenges for robot perception in dynamic, unstructured, and remote environments often encountered in field robotics applications. I will present my recent research to overcome these challenges to advance perceptual capabilities of robotic systems across sea, land, and space. Lastly, I will share my insight on opportunities to integrate learning-based approaches into field robotic systems for practical deployment.
Bio: Dr. Katherine (Katie) Skinner is an Assistant Professor in the Department of Robotics at the University of Michigan. Prior to this appointment, she was a Postdoctoral Fellow in the Daniel Guggenheim School of Aerospace Engineering and the School of Earth and Atmospheric Sciences at Georgia Institute of Technology. She received an M.S. and Ph.D. from the Robotics Institute at the University of Michigan, and a B.S.E. in Mechanical and Aerospace Engineering with a Certificate in Applications of Computing from Princeton University.