ChatSim: Underwater Simulation with Natural Language Prompting
Aadi Palnitkar, Rashmi Kapu, Xiaomin Lin, Cheng Liu, Nare Karapetyan,, Yiannis Aloimonos

TL;DR
ChatSim integrates large language models with underwater simulation to enable intuitive environment control via natural language, facilitating cost-effective testing for marine exploration and research.
Contribution
It introduces a novel method combining LLMs with OysterSim to allow natural language control of underwater simulations, enhancing usability and flexibility.
Findings
Enables intuitive environment creation through natural language prompts.
Reduces time and cost for underwater simulation testing.
Improves simulation realism and control for marine research.
Abstract
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. \invis{Moreover,…
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Taxonomy
TopicsTopic Modeling · Explainable Artificial Intelligence (XAI) · Context-Aware Activity Recognition Systems
