air-embodied-brain/Zetta-Embodiment

Minimal test environment

Python 3.11 through 3.12 is supported. The minimal test environment (no simulator/model required):

python -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install -e ".[test]"

LLM provider

Campaigns call LLM agents in two places, configured separately:

• Offline campaign stages (Cluster, Diagnose, Stage 2) always use the Codex planner (openai-codex). Set CODEX_API_KEY, plus CODEX_BASE_URL for an OpenAI-compatible gateway; run_campaign.py --model selects the model. To fail over across several routes, set ZETTA_API_PROVIDERS to a JSON list of routes (name, model, base_url, and api_key or api_key_env).
• Online Role1 reviews Critic proposals during candidate rollouts. The LIBERO, RoboCasa and RoboTwin preparation scripts freeze its backend via --role1-planner api|codex and --role1-model. The api planner takes a provider-prefixed model id (e.g. openai:<model>) and reads the provider's own key (OPENAI_API_KEY, ANTHROPIC_API_KEY, …); on LIBERO and RoboCasa, ZETTA_ROLE1_BASE_URL overrides its base URL. The codex planner uses the CODEX_* variables above.

Store these secrets as described in Security and Reproducibility.

VLA runtime environment (LIBERO-Pro or RoboCasa)

scripts/deployment/install_vla_env.sh builds a venv for one of two tracks. See the VLA runtime setup guide for the full installation notes, compatibility fixes, and known limitations.

[!WARNING] The two tracks cannot share a venv state (incompatible robosuite versions).

Prebuilt Docker images, built from scripts/deployment/Dockerfile.vla-env, are available for both tracks so you can skip the manual venv build:

• LIBERO-Pro image: preconfigured with the LIBERO-Pro simulator stack and Pi0.5 dependencies. Baidu Netdisk (百度网盘)
• RoboCasa image: preconfigured with the RoboCasa/robosuite/Isaac-GR00T stack. Baidu Netdisk (百度网盘)

LIBERO-Pro + Pi0.5

export REPO_ROOT=/abs/path/to/Zetta-Embodiment
export VENV_ROOT=/abs/path/to/venvs/vla-env
bash scripts/deployment/install_vla_env.sh --track libero-pro

install_vla_env.sh downloads the LIBERO-Pro scene/object assets automatically. To fetch or refresh them manually:

"$VENV_ROOT/bin/liberopro-download-assets" --skip-existing

RoboCasa + GR00T (needs a source checkout with robocasa/, robosuite/, Isaac-GR00T/)

export REPO_ROOT=/abs/path/to/Zetta-Embodiment
export VENV_ROOT=/abs/path/to/venvs/vla-env
export ROBOCASA_SRC_ROOT=/abs/path/to/robocasa-source-checkout
bash scripts/deployment/install_vla_env.sh --track robocasa

RoboCasa also needs its kitchen assets (~10GB); see the RoboCasa installation guide for details:

python -m robocasa.scripts.setup_macros              # set up system variables
python -m robocasa.scripts.download_kitchen_assets   # downloads ~10GB of kitchen assets

RoboTwin 2.0 environment

RoboTwin 2.0 + Pi0.5

RoboTwin is SAPIEN-based and cannot share a venv with either track above. Use the upstream RLinf image rather than building one:

git clone https://github.com/RoboTwin-Platform/RoboTwin.git -b RLinf_support
# then download ~15GB of assets into its assets/ directory

docker run --rm --gpus all --shm-size 32g \
  -e NVIDIA_DISABLE_REQUIRE=1 \
  -e NVIDIA_DRIVER_CAPABILITIES=compute,utility,graphics \
  -e LD_LIBRARY_PATH=/usr/lib/x86_64-linux-gnu:/usr/local/cuda/lib64 \
  --device /dev/dri \
  -v "$ROBOTWIN_ROOT":/workspace/RoboTwin \
  -v "$REPO_ROOT":/workspace/Zetta \
  --entrypoint /bin/bash rlinf/rlinf:agentic-rlinf0.4-robotwin

The image ships three venvs under /opt/venv/; this repository uses openpi. assets_path must point at the RoboTwin repository root, not its assets/ subdirectory.

<details> <summary><b>Why these environment variables?</b></summary>

The three environment variables let a CUDA 12.8 image run on a 12.4 driver: NVIDIA_DISABLE_REQUIRE skips the image's cuda>=12.8 label check, NVIDIA_DRIVER_CAPABILITIES drops the display capability that needs a /dev/nvidia-modeset a headless host does not have, and LD_LIBRARY_PATH puts the host driver's libcuda ahead of the image's forward-compatibility libraries — CUDA forward compatibility is datacenter-only and fails on GeForce with Error 804.

</details>

Genie Sim / Isaac Sim environment

The native geniesim Runtime backend supports the RoboColiseum g2op_if_pick_block_color task with Isaac Sim 5.1 and an isolated Python 3.11 simulator process. Use prepare_geniesim.py to prepare pinned dependencies and task assets, then run [smoke_gen

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