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ChatSim: Underwater Simulation with Natural Language Prompting

Aadi Palnitkar, Rashmi Kapu, Xiaomin Lin, Cheng Liu, Nare Karapetyan, Yiannis Aloimonos

TL;DR

This work addresses the barrier of coding underwater simulations by integrating a Large Language Model with the OysterSim/Blender pipeline to control a BlueROV via natural language. The authors implement a constrained function library and prompting framework to translate NL instructions into executable Python calls that manipulate the simulation and capture imagery. Four experiments demonstrate movement, object placement, region editing, and trajectory generation driven by NL prompts, highlighting improvements in accessibility and rapid scenario testing. The approach holds promise for accelerating marine robotics research and environmental monitoring by reducing setup time and enabling diverse, realistic test environments.

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, 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.

ChatSim: Underwater Simulation with Natural Language Prompting

TL;DR

This work addresses the barrier of coding underwater simulations by integrating a Large Language Model with the OysterSim/Blender pipeline to control a BlueROV via natural language. The authors implement a constrained function library and prompting framework to translate NL instructions into executable Python calls that manipulate the simulation and capture imagery. Four experiments demonstrate movement, object placement, region editing, and trajectory generation driven by NL prompts, highlighting improvements in accessibility and rapid scenario testing. The approach holds promise for accelerating marine robotics research and environmental monitoring by reducing setup time and enabling diverse, realistic test environments.

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, 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.
Paper Structure (14 sections, 8 figures, 1 table)

This paper contains 14 sections, 8 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Simulations and Datasets
  4. Transformers and LLMs in Robotics
  5. Approach
  6. Implementation Details
  7. Execution Pipeline
  8. Potential Risks
  9. Experiments
  10. Experiment 1
  11. Experiment 2
  12. Experiment 3
  13. Experiment 4
  14. Conclusions and Future Work

Figures (8)

  • Figure 1: Interaction between the user, Natural Language Model - ChatGPT, and the simulation environment - Blender.
  • Figure 2: Pipeline to create a simulation scene.
  • Figure 3: The flowchart of the actions taking place during the execution. The system eliminates the engineer 'middleman' by integrating LLMs into the pipeline.
  • Figure 4: An image depicting the simulation's initial startup process. To the left is the initial position and orientation of the agent (BlueROV) and to the right is the image depicting how the simulation starts on a terminal window.
  • Figure 5: The image on the left depicts the trajectory taken by the BlueROV when given a command to follow a circular path with a radius of 3 units, as illustrated in the image on the right.
  • ...and 3 more figures