Aaron Cofield
Papers
2
Total Citations
5
H-Index
2
About
Aaron Cofield is a robotics researcher specializing in human-robot interaction, perception, and autonomous manipulation. His work addresses critical challenges in enabling robots to operate effectively in unstructured and low-visibility environments. Cofield’s most cited paper, “A Humanoid Robot Object Perception Approach Using Depth Images” (2019, 3 citations), advances object grasping and manipulation for humanoid robots, a key capability for real-world deployment. He further tackles the problem of degraded camera performance in low-light conditions with “Image Preprocessing for Stereoscopic Visual Odometry at Night” (2018, 2 citations), proposing a simple yet effective preprocessing pipeline to enhance visual odometry accuracy after dark. Though early in his career, Cofield’s contributions bridge foundational perception gaps—from depth-based object recognition to robust nighttime navigation—laying groundwork for more resilient autonomous systems. His research is particularly relevant for students and engineers interested in practical, sensor-driven solutions for mobile manipulators and field robotics.
Research Focus
Key Achievements
Top Papers
- 1A Humanoid Robot Object Perception Approach Using Depth Images3 citations · 2019
- 2Image Preprocessing for Stereoscopic Visual Odometry at Night2 citations · 2018