Enabling Vision Guided Robotic Ocean Exploration
Bibliographic record
Abstract
This talk will describe ongoing research efforts in Dr. Girdhar's lab aimed at developing robotics and machine learning-based techniques to enable search, discovery, and mapping of hard to observe underwater natural phenomena. The focus will be on visual observations, which complicates the adaptive data collection process in many ways, some of which Dr. Girdhar will address in this talk. Furthermore, he will discuss an approach to modeling spatial distribution of high dimensional observations such as the distribution of phytoplankton taxa, while automatically discovering the community structure, and how such an approach could be used by future robots to better sample microscopic organisms in the ocean. Presenter Bio Yogesh Girdhar is a computer scientist and the PI of the WARP Lab (http://warp.whoi.edu) at Woods Hole Oceanographic Institution (WHOI), and an Associate Scientist (without Tenure) in the Applied Ocean Physics & Engineering department. He received his BS and MS from Rensselaer Polytechnic Institute in Troy, NY; and his Ph.D. from McGill University in Montreal, Canada. During his Ph.D. Girdhar developed an interest in ocean exploration using autonomous underwater vehicles, which motivated him to come to WHOI, initially as a postdoc, and then later continue as a scientist to start WARPLab. Girdhar’s research has since then focused on developing smarter autonomous exploration robots that can accelerate the scientific discovery process in extreme and challenging environments, such as the deep sea. Some notable recognition of his work includes the Best Paper Award in Service Robotics at ICRA 2020, a finalist for Best Paper Award at IROS 2018, and honorable mention for the 2014 CIPPRS Doctoral Dissertation Award
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".