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Hayat Rajani

dblp:341/1179 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Robotics › Robot manipulation › robot simulation
sensor simulation
0.912025
Stonefish: Supporting Machine Learning Research in Marine Robotics · ICRA 2025
Machine learning › Reinforcement learning › reinforcement learning environment
simulation environment
0.912025
Stonefish: Supporting Machine Learning Research in Marine Robotics · ICRA 2025
Robotics › Legged, aerial and field robots
underwater robotics
0.912025
Stonefish: Supporting Machine Learning Research in Marine Robotics · ICRA 2025
Machine learning › Deep learning architectures and training › data-centric deep learning
training data generation
0.312025
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
YearPublicationVenuePosition
2025 Stonefish: Supporting Machine Learning Research in Marine Robotics
abstract
Simulations 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
ICRA5