examples/libero/README.md

LIBERO Benchmark

This example runs the LIBERO benchmark: https://github.com/Lifelong-Robot-Learning/LIBERO

Note: When updating requirements.txt in this directory, there is an additional flag --extra-index-url https://download.pytorch.org/whl/cu113 that must be added to the uv pip compile command.

This example requires git submodules to be initialized. Don't forget to run:

git submodule update --init --recursive

With Docker (recommended)

# Grant access to the X11 server:
sudo xhost +local:docker

# To run with the default checkpoint and task suite:
SERVER_ARGS="--env LIBERO" docker compose -f examples/libero/compose.yml up --build

# To run with glx for Mujoco instead (use this if you have egl errors):
MUJOCO_GL=glx SERVER_ARGS="--env LIBERO" docker compose -f examples/libero/compose.yml up --build

You can customize the loaded checkpoint by providing additional SERVER_ARGS (see scripts/serve_policy.py), and the LIBERO task suite by providing additional CLIENT_ARGS (see examples/libero/main.py). For example:

# To load a custom checkpoint (located in the top-level openpi/ directory):
export SERVER_ARGS="--env LIBERO policy:checkpoint --policy.config pi05_libero --policy.dir ./my_custom_checkpoint"

# To run the libero_10 task suite:
export CLIENT_ARGS="--args.task-suite-name libero_10"

Without Docker (not recommended)

Terminal window 1:

# Create virtual environment
uv venv --python 3.8 examples/libero/.venv
source examples/libero/.venv/bin/activate
uv pip sync examples/libero/requirements.txt third_party/libero/requirements.txt --extra-index-url https://download.pytorch.org/whl/cu113 --index-strategy=unsafe-best-match
uv pip install -e packages/openpi-client
uv pip install -e third_party/libero
export PYTHONPATH=$PYTHONPATH:$PWD/third_party/libero

# Run the simulation
python examples/libero/main.py

# To run with glx for Mujoco instead (use this if you have egl errors):
MUJOCO_GL=glx python examples/libero/main.py

Terminal window 2:

# Run the server
uv run scripts/serve_policy.py --env LIBERO

Results

If you want to reproduce the following numbers, you can evaluate the checkpoint at gs://openpi-assets/checkpoints/pi05_libero/. This checkpoint was trained in openpi with the pi05_libero config.

Model	Libero Spatial	Libero Object	Libero Goal	Libero 10	Average
π0.5 @ 30k (finetuned)	98.8	98.2	98.0	92.4	96.85