# DAVE Aquatic Virtual Environment: Toward a General Underwater Robotics Simulator

arXiv: https://arxiv.org/abs/2209.02862 · HTML: https://mlsanity.com/paper/2209.02862/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Sep 2022.

## Built on by (most cited first, 11 of 11)

- [A Survey of Robotic Navigation and Manipulation with Physics Simulators in the Era of Embodied AI](https://mlsanity.com/paper/2505.01458/) (2025)
- [Stonefish: Supporting Machine Learning Research in Marine Robotics](https://mlsanity.com/paper/2502.11887/) (2025)
- [MarineGym: A High-Performance Reinforcement Learning Platform for Underwater Robotics](https://mlsanity.com/paper/2503.09203/) (2025)
- [Learning to Swim: Reinforcement Learning for 6-DOF Control of Thruster-driven Autonomous Underwater Vehicles](https://mlsanity.com/paper/2410.00120/) (2024)
- [Sonar-based Deep Learning in Underwater Robotics: Overview, Robustness and Challenges](https://mlsanity.com/paper/2412.11840/) (2024)
- [MARVIS: Motion & Geometry Aware Real and Virtual Image Segmentation](https://mlsanity.com/paper/2403.09850/) (2024)
- [Improving Generalization of Synthetically Trained Sonar Image Descriptors for Underwater Place Recognition](https://mlsanity.com/paper/2308.01058/) (2023)
- [Learning Underwater Active Perception in Simulation](https://mlsanity.com/paper/2504.17817/) (2025)
- [UEOF: A Benchmark Dataset for Underwater Event-Based Optical Flow](https://mlsanity.com/paper/2601.10054/) (2026)
- [Sim2Sea: Sim-to-Real Policy Transfer for Maritime Vessel Navigation in Congested Waters](https://mlsanity.com/paper/2603.04057/) (2026)
- [A Simulation Platform for AUV Fault Recovery: Exploring LLM-Based Diagnostic Strategies](https://mlsanity.com/paper/2609.20620/) (2026)

## Abstract (arXiv)

We present DAVE Aquatic Virtual Environment (DAVE), an open source simulation stack for underwater robots, sensors, and environments. Conventional robotics simulators are not designed to address unique challenges that come with the marine environment, including but not limited to environment conditions that vary spatially and temporally, impaired or challenging perception, and the unavailability of data in a generally unexplored environment. Given the variety of sensors and platforms, wheels are often reinvented for specific use cases that inevitably resist wider adoption. Building on existing simulators, we provide a framework to help speed up the development and evaluation of algorithms that would otherwise require expensive and time-consuming operations at sea. The framework includes basic building blocks (e.g., new vehicles, water-tracking Doppler Velocity Logger, physics-based multibeam sonar) as well as development tools (e.g., dynamic bathymetry spawning, ocean currents), which allows the user to focus on methodology rather than software infrastructure. We demonstrate usage through example scenarios, bathymetric data import, user interfaces for data inspection and motion planning for manipulation, and visualizations.