Papers
15
Total Citations
186
H-Index
8
About
Michael Fulton is a robotics and computer vision researcher whose work sits at the intersection of marine robotics, underwater human-robot interaction, and environmental AI. He is perhaps best known for developing the **TrashCan** dataset series — a comprehensive, semantically segmented collection of underwater debris imagery that has become a foundational resource for marine litter detection research, accumulating over 57 citations since its 2020 release. His early contributions, including Trash-ICRA19, helped establish the data infrastructure necessary for training robust deep learning models in challenging underwater environments. Beyond environmental monitoring, Fulton has made significant strides in enabling meaningful collaboration between autonomous underwater vehicles (AUVs) and human divers. His work spans diver detection using deep neural networks, motion prediction for safer robot navigation, and novel communication frameworks — including gestural languages and the innovative SIREN audio system, which transforms a robot's outer hull into a speaker. His 2022 study on motion-based robot-to-human communication further extended these ideas to field robotics more broadly. With over 160 cumulative citations across his most prominent works, Fulton's research meaningfully advances both ocean conservation technology and the human-robot collaboration capabilities essential for real-world underwater operations.
Research Focus
Key Achievements
Top Papers
- 1
- 2A Generative Approach Towards Improved Robotic Detection of Marine Litter25 citations · 2020
- 3
- 4TrashCan 1.0 An Instance-Segmentation Labeled Dataset of Trash Observations12 citations · 2019
- 5Trash-ICRA19: A bounding box labeled dataset of underwater trash11 citations · 2018
- 6An Analysis of Deep Object Detectors For Diver Detection11 citations · 2020
- 7
- 8
- 9SIREN: Underwater Robot-to-Human Communication Using Audio7 citations · 2023
- 10