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How to run a robot task from a browser console with EMERGE Policy

Start the Web console, create a conversation, and give the robot a task with live camera views.

  • Top pick
  • Research
  • Intermediate
  • 3 hours
  • From the README
  • Any agent

Who it is forA researcher or operator who wants a visual workspace for daily robot task runs in simulation without writing code.

The problemManaging model services, controllers, and scene state by hand for each experiment is slow and easy to get wrong.

The outcomeThe robot attempts the typed task in an isolated scene, with live camera views, conversation, and task progress visible in the browser.

What you need first

  • Conda and Python 3.12 for the EmergePolicy environment
  • GPUs assigned by index for the model services (OPENPI_GPU, VGGT_GPU, SAM3_GPU)
  • Downloaded model checkpoints placed under checkpoints/ as listed in the README
  • LIBERO-Plus assets.zip downloaded from Hugging Face and extracted into the submodule
  • API configuration, which the first launch of Web or TUI walks you through

Steps

  1. Clone the repository with git clone --recurse-submodules https://github.com/EMERGE-Policy/EMERGE-Policy.git and then run git submodule update --init --recursive. (inferred, check the README)
  2. Create and activate the environment with conda create -n EmergePolicy python=3.12 -y and conda activate EmergePolicy.
  3. Start the model services for OpenPI VLA with OPENPI_GPU=0 VGGT_GPU=1 SAM3_GPU=2 bash scripts/model_server/start_external_model_servers.sh --services openpi,vggt,sam3. (inferred, check the README)
  4. Launch the browser console with emerge web.
  5. Open http://127.0.0.1:8080/, complete the first-run API setup, create a conversation, and enter a task.

Prompt for your agent

In the EMERGE-Policy repo, follow the README Quick Start to start the Web console with `emerge web`, create a conversation, and submit the task "<task>". Report the scene that was created and the task progress shown in the console. Verify that the conversation has its own isolated scene and workspace before reporting success.

Caveat. The first run needs several large model checkpoints and GPUs assigned to the model services, so setup takes much longer than the launch command itself.

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