ChatSim: Underwater Simulation with Natural Language Prompting
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Abstract (arXiv)
Robots are becoming an essential part of many operations including marine exploration or environmental monitoring. However, the underwater environment presents many challenges, including high pressure, limited visibility, and harsh conditions that can damage equipment. Real-world experimentation can be expensive and difficult to execute. Therefore, it is essential to simulate the performance of underwater robots in comparable environments to ensure their optimal functionality within practical real-world contexts.OysterSim generates photo-realistic images and segmentation masks of objects in marine environments, providing valuable training data for underwater computer vision applications. By integrating ChatGPT into underwater simulations, users can convey their thoughts effortlessly and intuitively create desired underwater environments without intricate coding. invisMoreover, researchers can realize substantial time and cost savings by evaluating their algorithms across diverse underwater conditions in the simulation. The objective of ChatSim is to integrate Large Language Models (LLM) with a simulation environment~(OysterSim), enabling direct control of the simulated environment via natural language input. This advancement can greatly enhance the capabilities of underwater simulation, with far-reaching benefits for marine exploration and broader scientific research endeavors.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Aug 2023.
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Citing papers per year: 2023: 1; 2024: 3; 2025: 2.
6 papers.
- 2023A Safer Vision-based Autonomous Planning System for Quadrotor UAVs with Dynamic Obstacle Trajectory Prediction and Its Application with LLMscited by 13
- 2024Exploring Boundary of GPT-4V on Marine Analysis: A Preliminary Case Studycited by 9
- 2024MARVIS: Motion & Geometry Aware Real and Virtual Image Segmentationcited by 3
- 2024Integrating Large Language Models for UAV Control in Simulated Environments: A Modular Interaction Approachcited by 3
- 2025Enabling On-Device Medical AI Assistants via Input-Driven Saliency Adaptationcited by 2
- 2025MarineEval: Assessing the Marine Intelligence of Vision-Language Modelscited by 1
- 2025MarineEval: Assessing the Marine Intelligence of Vision-Language Models24 Dec 2025
- 2025Enabling On-Device Medical AI Assistants via Input-Driven Saliency Adaptation7 Jun 2025
- 2024Integrating Large Language Models for UAV Control in Simulated Environments: A Modular Interaction Approach23 Oct 2024
- 2024MARVIS: Motion & Geometry Aware Real and Virtual Image Segmentation14 Mar 2024
- 2024Exploring Boundary of GPT-4V on Marine Analysis: A Preliminary Case Study4 Jan 2024
- 2023A Safer Vision-based Autonomous Planning System for Quadrotor UAVs with Dynamic Obstacle Trajectory Prediction and Its Application with LLMs21 Nov 2023