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What you can do with EMERGE Policy

EMERGE-Policy is a Python embodied-agent framework where a primary agent plans and calls specialized perception, action, and verification models to control robots in LIBERO simulation.

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EMERGE-Policy/EMERGE-Policy on GitHub

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What do you want to do?

4 use cases

  • Run a robot task from a browser console

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

    View prompt
    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.

  • Run one robot task headlessly and get JSON back

    Send one natural-language task to a running Controller and read the JSON result from stdout.

    View prompt
    In the EMERGE-Policy repo, with the Controller already running, execute `python -m Emerge.cli.headless "<task>"` and capture stdout. Expected output is a JSON result for the task. Verify that the output parses as valid JSON before reporting it.

  • Reset and switch simulation scenes from the terminal

    Use the terminal interface to reload the environment or pick a different scene without restarting.

    View prompt
    In the EMERGE-Policy repo, open the TUI with `emerge` and run `/scene` to select the scene named "<scene>", then run a task. Expected output is the selected scene loaded with plan and robot state shown. Verify that the scene name in the interface matches the requested one before continuing.

  • Evaluate the agent on LIBERO and its robustness variants

    Follow the README evaluation guides to test EMERGE Policy on standard LIBERO, LIBERO-Plus, and LIBERO-Pro.

    View prompt
    In the EMERGE-Policy repo, read scripts/LiberoPlus_eval/README.md and run the evaluation it describes for the suite "<suite>". Expected output is an evaluation result file or summary from that suite. Verify that the LIBERO-Plus assets are present under third_party/libero_plus before starting.

How to use EMERGE Policy

  1. Check you have what it needs: 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.
  2. Open EMERGE-Policy/EMERGE-Policy on GitHub and read the README and license.
  3. 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)
  4. Create and activate the environment with conda create -n EmergePolicy python=3.12 -y and conda activate EmergePolicy.
  5. 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)
  6. Launch the browser console with emerge web.

These steps come from our top use case, Run a robot task from a browser console, which also has a copy-paste agent prompt.

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About this repo

Requirements

  • 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
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Questions about EMERGE Policy

What is EMERGE Policy?

EMERGE-Policy is a Python embodied-agent framework where a primary agent plans and calls specialized perception, action, and verification models to control robots in LIBERO simulation. It is an open-source project on GitHub: https://github.com/EMERGE-Policy/EMERGE-Policy.

Who is EMERGE Policy for?

Robotics researchers and engineers who want to run language-driven robot tasks in simulation, or benchmark embodied policies on LIBERO variants.

What can I do with EMERGE Policy?

4 use cases so far, for example: Run a robot task from a browser console; Run one robot task headlessly and get JSON back; Reset and switch simulation scenes from the terminal. Each has steps and a copy-paste agent prompt.

What do I need to run EMERGE Policy?

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.

What are alternatives to EMERGE Policy?

Other hot AI repos in Research: Scientific Agent Skills, Speaker Mem R1, DSH Council, Evo Bot, AI Engineering Course.

Is EMERGE Policy safe to run?

Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.