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

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

export SERVER_ARGS="--env LIBERO"
docker compose -f examples/libero/compose.yml up --build

Without Docker

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

Terminal window 2:

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

Results

If you follow the training instructions and hyperparameters in the pi0_libero and pi0_fast_libero configs, you should get results similar to the following:

Model	Libero Spatial	Libero Object	Libero Goal	Libero 10	Average
π0-FAST @ 30k (finetuned)	96.4	96.8	88.6	60.2	85.5
π0 @ 30k (finetuned)	96.8	98.8	95.8	85.2	94.15

Note that the hyperparameters for these runs are not tuned and $\pi_0$-FAST does not use a FAST tokenizer optimized for Libero. Likely, the results could be improved with more tuning, we mainly use these results as an example of how to use openpi to fine-tune $\pi_0$ models on a new dataset.