SimScene

Describe a scene. Find assets online. Build, simulate, and film it.

SimScene gives AI agents tools to discover simulation assets, compose editable USD scenes, run Isaac Sim, and improve the result from real images and physical observations. It connects online asset selection to scene creation and video delivery through MCP and reusable agent skills.

中文快速开始 · Plugin setup · Examples · Apache-2.0

Watch the chain reaction · Watch the sorting workshop

What you can build

The project has completed a stage covering online asset composition, physical interaction validation, and multi-camera film delivery, with reusable production tools. A token advantage across tasks remains to be validated in real use.

Related MCP server: qfoldit-unigine-toolbelt

Get started

Python 3.10+ is required. Running simulations also requires a compatible NVIDIA Isaac Sim installation on a GPU host. Install FFmpeg for video capture and editing; see video setup for encoding requirements.

git clone https://github.com/ros-claw/simscene.git
cd simscene
python3 -m venv .venv
. .venv/bin/activate
python -m pip install -e . usd-core

simscene configure --isaac-python /absolute/path/to/isaac-sim/python.sh
simscene doctor
simscene search 'industrial conveyor'

Search returns candidates and metadata. After choosing a result, inspect it and acquire only the selected files:

simscene inspect 'ASSET_URL_FROM_SEARCH'
simscene acquire 'ASSET_URL_FROM_SEARCH'

The default service is NVIDIA USD Search. Set USD_SEARCH_API_URL to use another endpoint. Check each asset's dependency status and license before use. Downloaded models and textures are not bundled with this repository.

Connect your agent

Run the MCP server from the installed environment:

simscene mcp

For an MCP client that accepts a JSON server configuration:

{
  "mcpServers": {
    "simscene": {
      "command": "/absolute/path/to/simscene/.venv/bin/python",
      "args": ["-m", "simscene.cli", "mcp"]
    }
  }
}

Use the build-scene skill to discover assets, compose a scene, run it, and inspect the result. Use deliver-scene-video to plan cameras and produce traceable films. Packaging and installation covers portable, Claude Code, and DeepSeek Harness integration.

Try a prompt such as:

Build a small workshop with a conveyor, collection bins, and a camera that clearly shows the handoff. Find suitable assets online, inspect their scale and dependencies, run the scene, and improve it from the observations.

Examples

Recipes preserve the selected asset references and scene operations. Acquire the referenced assets on your own simulation host and use the example's case to check a new run.

Remote simulation

The MCP service can run on a GPU workstation while assets, USD files, and caches stay on that host. Clients receive images, state, and video data. For the experimental HTTP transport, bind through an SSH tunnel as described in the remote setup guide. A local SSH tunnel has been tested; cross-machine deployment still needs validation in the target environment.

Documentation

• Quick start / 快速开始

• Plugin packaging and installation

• Cameras, video, and layout

• Validation scope

• Repository contents and asset distribution

• Contributing

SimScene is a development project. Scene plans and successful worker exits need physical verification against the intended task. Robot following and collision checks are experimental; their measured scope is described in the validation documentation.

License

SimScene code is licensed under Apache-2.0. Third-party assets retain their own terms; see NOTICE.