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Josie Giles
IRIM Marketing Communications Manager
josie@gatech.edu
Summary Sentence: “Learning from the Field: Physically-based Deep Learning to Advance Robot Vision in Natural Environments”
Full Summary: The Institute for Robotics and Intelligent Machines presents “Learning from the Field: Physically-based Deep Learning to Advance Robot Vision in Natural Environments” by Katherine Skinner of Georgia Tech. The event will be held in the Marcus Nanotechnology Building, Rooms 1116-1118, from 12:15-1:15 p.m. and is open to the public.
Katherine SkinnerThe Institute for Robotics and Intelligent Machines presents “Learning from the Field: Physically-based Deep Learning to Advance Robot Vision in Natural Environments” by Katherine Skinner of Georgia Tech. The event will be held in the Marcus Nanotechnology Building, Rooms 1116-1118, from 12:15-1:15 p.m. and is open to the public.
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 robotic 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 my work to develop approaches for unsupervised learning to advance perceptual capabilities of robots in underwater environments. The underwater domain presents unique environmental conditions to robotic systems that exacerbate the challenges in perception for field robotics. To address these challenges, I leverage physics-based models and cross-disciplinary knowledge about the physical environment and the data collection process to provide constraints that relax the need for ground truth labels. This leads to a hybrid model-based, data-driven solution. I will also present work that relates this framework to challenges for autonomous vehicles in other domains.
Bio
Katherine Skinner is a Postdoctoral Fellow in Aerospace Engineering at Georgia Tech. She received her Ph.D. from the Robotics Institute at the University of Michigan in 2019. She also holds a B.S.E. in Mechanical and Aerospace Engineering with a Certificate in Applications of Computing from Princeton University and an M.S. in Robotics from the University of Michigan. She is a recipient of the NSF EAPSI Fellowship.
