DAVE Aquatic Virtual Environment: Toward a General Underwater Robotics Simulator
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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.
Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). First posted on arXiv 6 Sep 2022.
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Citing papers per year: 2023: 1; 2024: 3; 2025: 4; 2026: 3.
11 papers.
- 2025A Survey of Robotic Navigation and Manipulation with Physics Simulators in the Era of Embodied AIcited by 16
- 2025Stonefish: Supporting Machine Learning Research in Marine Roboticscited by 11
- 2025MarineGym: A High-Performance Reinforcement Learning Platform for Underwater Roboticscited by 8
- 2024Learning to Swim: Reinforcement Learning for 6-DOF Control of Thruster-driven Autonomous Underwater Vehiclescited by 6
- 2024Sonar-based Deep Learning in Underwater Robotics: Overview, Robustness and Challengescited by 5
- 2024MARVIS: Motion & Geometry Aware Real and Virtual Image Segmentationcited by 3
- 2023Improving Generalization of Synthetically Trained Sonar Image Descriptors for Underwater Place Recognitioncited by 1
- 2025Learning Underwater Active Perception in Simulationcited by 0
- 2026UEOF: A Benchmark Dataset for Underwater Event-Based Optical Flowcited by 0
- 2026Sim2Sea: Sim-to-Real Policy Transfer for Maritime Vessel Navigation in Congested Waterscited by 0
- 2026A Simulation Platform for AUV Fault Recovery: Exploring LLM-Based Diagnostic Strategiescited by 0
- 2026A Simulation Platform for AUV Fault Recovery: Exploring LLM-Based Diagnostic Strategies17 Sep 2026
- 2026Sim2Sea: Sim-to-Real Policy Transfer for Maritime Vessel Navigation in Congested Waters4 Mar 2026
- 2026UEOF: A Benchmark Dataset for Underwater Event-Based Optical Flow15 Jan 2026
- 2025A Survey of Robotic Navigation and Manipulation with Physics Simulators in the Era of Embodied AI1 May 2025
- 2025Learning Underwater Active Perception in Simulation23 Apr 2025
- 2025MarineGym: A High-Performance Reinforcement Learning Platform for Underwater Robotics12 Mar 2025
- 2025Stonefish: Supporting Machine Learning Research in Marine Robotics17 Feb 2025
- 2024Sonar-based Deep Learning in Underwater Robotics: Overview, Robustness and Challenges16 Dec 2024
- 2024Learning to Swim: Reinforcement Learning for 6-DOF Control of Thruster-driven Autonomous Underwater Vehicles30 Sep 2024
- 2024MARVIS: Motion & Geometry Aware Real and Virtual Image Segmentation14 Mar 2024
- 2023Improving Generalization of Synthetically Trained Sonar Image Descriptors for Underwater Place Recognition2 Aug 2023