Learning from the Field: Deep Learning for Robot Vision in Natural Environments

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 on land.

BIO: Katherine Skinner is a Ph.D. Candidate in the Robotics Institute at the University of Michigan and a member of the Deep Robot Optical Perception Lab. Her research interests span robotics, machine learning, and computer vision, with a focus on enabling autonomy in dynamic, unstructured, or remote environments. She received a B.S.E. in Mechanical and Aerospace Engineering with a Certificate in Applications of Computing from Princeton University in 2014 and an M.S. in Robotics from the University of Michigan in 2016. She has held appointments at Woods Hole Oceanographic Institution and the Australian Centre for Field Robotics. She is a recipient of the NSF EAPSI Fellowship.

Learning from the Field: Deep Learning for Robot Vision in Natural Environments

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 on land.

BIO: Katherine Skinner is a Ph.D. Candidate in the Robotics Institute at the University of Michigan and a member of the Deep Robot Optical Perception Lab. Her research interests span robotics, machine learning, and computer vision, with a focus on enabling autonomy in dynamic, unstructured, or remote environments. She received a B.S.E. in Mechanical and Aerospace Engineering with a Certificate in Applications of Computing from Princeton University in 2014 and an M.S. in Robotics from the University of Michigan in 2016. She has held appointments at Woods Hole Oceanographic Institution and the Australian Centre for Field Robotics. She is a recipient of the NSF EAPSI Fellowship.