Demo Data from EDBT 2026 / reviewers in the wild / expert
Hayat Rajani
dblp:341/1179
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
0000-0002-2541-2787ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Systems, architecture and hardware · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Legged, aerial and field robots · 30% Reinforcement learning · 30% Robot manipulation · 30% |
Topics — the 4 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Robot manipulation › robot simulation
sensor simulation |
0.9 | 1 | 2025 | Stonefish: Supporting Machine Learning Research in Marine Robotics · ICRA 2025 |
Machine learning › Reinforcement learning › reinforcement learning environment
simulation environment |
0.9 | 1 | 2025 | Stonefish: Supporting Machine Learning Research in Marine Robotics · ICRA 2025 |
Robotics › Legged, aerial and field robots
underwater robotics |
0.9 | 1 | 2025 | Stonefish: Supporting Machine Learning Research in Marine Robotics · ICRA 2025 |
Machine learning › Deep learning architectures and training › data-centric deep learning
training data generation |
0.3 | 1 | 2025 | Stonefish: Supporting Machine Learning Research in Marine Robotics · ICRA 2025 |
Methods — techniques the papers use, named apart from their topics
sonar simulation · 0.9hydrodynamics modeling · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Stonefish: Supporting Machine Learning Research in Marine RoboticsabstractSimulations are highly valuable in marine robotics, offering a cost-effective and controlled environment for testing in the challenging conditions of underwater and surface operations. Given the high costs and logistical difficulties of real-world trials, simulators capable of capturing the operational conditions of subsea environments have become key in developing and refining algorithms for remotely-operated and autonomous underwater vehicles. This paper highlights recent enhancements to the Stonefish simulator, an advanced open-source platform supporting development and testing of marine robotics solutions. Key updates include a suite of additional sensors, such as an event-based camera, a thermal camera, and an optical flow camera, as well as, visual light communication, support for tethered operations, improved thruster modelling, more flexible hydrodynamics, and enhanced sonar accuracy. These developments and an automated annotation tool significantly bolster Stonefish's role in marine robotics research, especially in the field of machine learning, where training data with a known ground truth is hard or impossible to collect. https://github.com/patrykcieslak/stonefish Michele Grimaldi, Patryk Cieslak, Eduardo Ochoa, Vibhav Bharti, Hayat Rajani, Ignacio Carlucho, Maria Koskinopoulou, Yvan R. Petillot, Nuno Gracias |
ICRA | 5 |