Andrew Garrett Kurbis
Papers
9
Total Citations
96
H-Index
6
About
Andrew Garrett Kurbis is a researcher specializing in computer vision, deep learning, and human-robot locomotion, with a particular focus on enabling robotic prosthetic legs and exoskeletons to navigate complex real-world environments. His work addresses one of the most persistent challenges in assistive robotics: enabling seamless, safe transitions between locomotion modes — particularly when approaching or descending stairs. Kurbis has made significant contributions through the development of sophisticated visual perception systems powered by convolutional neural networks, including the widely referenced StairNet framework, which has garnered 23 citations since its 2024 publication. His earlier stair recognition system (2022) laid the groundwork for the field, accumulating 28 citations and establishing egocentric vision as a viable sensing modality for wearable robotic systems. Notably, he has contributed to some of the largest open-source walking environment datasets, including ExoNet and StairNet, which have become foundational resources for the research community. Beyond static classification, Kurbis has pioneered temporal neural network approaches and semi-supervised learning strategies to improve perception efficiency on resource-constrained edge devices. With over 85 cumulative citations across his body of work, his research is shaping the future of intelligent, autonomous assistive robotics.
Research Focus
Key Achievements
Top Papers
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- 2StairNet: visual recognition of stairs for human–robot locomotion23 citations · 2024
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