openpi

openpi holds open-source models and packages for robotics, published by the Physical Intelligence team.

Currently, this repo contains three types of models: - the π₀ model, a flow-based vision-language-action model (VLA). - the π₀-FAST model, an autoregressive VLA, based on the FAST action tokenizer. - the π₀.₅ model, an upgraded version of π₀ with better open-world generalization trained with knowledge insulation. Note that, in this repository, we currently only support the flow matching head for both $\pi_{0.5}$ training and inference.

For all models, we provide base model checkpoints, pre-trained on 10k+ hours of robot data, and examples for using them out of the box or fine-tuning them to your own datasets.

This is an experiment: $\pi_0$ was developed for our own robots, which differ from the widely used platforms such as ALOHA and DROID, and though we are optimistic that researchers and practitioners will be able to run creative new experiments adapting $\pi_0$ to their own platforms, we do not expect every such attempt to be successful. All this is to say: $\pi_0$ may or may not work for you, but you are welcome to try it and see!

Updates

• [Sept 2025] We released PyTorch support in openpi.
• [Sept 2025] We released pi05, an upgraded version of pi0 with better open-world generalization.
• [Sept 2025]: We have added an improved idle filter for DROID training.
• [Jun 2025]: We have added instructions for using openpi to train VLAs on the full DROID dataset. This is an approximate open-source implementation of the training pipeline used to train pi0-FAST-DROID.

Requirements

To run the models in this repository, you will need an NVIDIA GPU with at least the following specifications. These estimations assume a single GPU, but you can also use multiple GPUs with model parallelism to reduce per-GPU memory requirements by configuring fsdp_devices in the training config. Please also note that the current training script does not yet support multi-node training.

Mode	Memory Required	Example GPU
Inference	> 8 GB	RTX 4090
Fine-Tuning (LoRA)	> 22.5 GB	RTX 4090
Fine-Tuning (Full)	> 70 GB	A100 (80GB) / H100

The repo has been tested with Ubuntu 22.04, we do not currently support other operating systems.

Installation

When cloning this repo, make sure to update submodules:

git clone --recurse-submodules git@github.com:Physical-Intelligence/openpi.git

# Or if you already cloned the repo:
git submodule update --init --recursive

We use uv to manage Python dependencies. See the uv installation instructions to set it up. Once uv is installed, run the following to set up the environment:

GIT_LFS_SKIP_SMUDGE=1 uv sync
GIT_LFS_SKIP_SMUDGE=1 uv pip install -e .

NOTE: GIT_LFS_SKIP_SMUDGE=1 is needed to pull LeRobot as a dependency.

Docker: As an alternative to uv installation, we provide instructions for installing openpi using Docker. If you encounter issues with your system setup, consider using Docker to simplify installation. See Docker Setup for more details.

Model Checkpoints

Base Models

We provide multiple base VLA model checkpoints. These checkpoints have been pre-trained on 10k+ hours of robot data, and can be used for fine-tuning.

Model	Use Case	Description	Checkpoint Path
$\pi_0$	Fine-Tuning	Base π₀ model for fine-tuning	gs://openpi-assets/checkpoints/pi0_base
$\pi_0$-FAST	Fine-Tuning	Base autoregressive π₀-FAST model for fine-tuning	gs://openpi-assets/checkpoints/pi0_fast_base
$\pi_{0.5}$	Fine-Tuning	Base π₀.₅ model for fine-tuning	gs://openpi-assets/checkpoints/pi05_base

Fine-Tuned Models

We also provide "expert" checkpoints for various robot platforms and tasks. These models are fine-tuned from the base models above and intended to run directly on the target robot. These may or may not work on your particular robot. Since these checkpoints were fine-tuned on relatively small datasets collected with more widely available robots, such as ALOHA and the DROID Franka setup, they might not generalize to your particular setup, though we found some of these, especially the DROID checkpoint, to generalize quite broadly in practice.

Model	Use Case	Description	Checkpoint Path
$\pi_0$-FAST-DROID	Inference	$\pi_0$-FAST model fine-tuned on the DROID dataset: can perform a wide range of simple table-top manipulation tasks 0-shot in new scenes on the DROID robot platform	gs://openpi-assets/checkpoints/pi0_fast_droid
$\pi_0$-DROID	Fine-Tuning	$\pi_0$ model fine-tuned on the DROID dataset: faster inference than $\pi_0$-FAST-DROID, but may not follow language commands as well	gs://openpi-assets/checkpoints/pi0_droid
$\pi_0$-ALOHA-towel	Inference	$\pi_0$ model fine-tuned on internal ALOHA data: can fold diverse towels 0-shot on ALOHA robot platforms	gs://openpi-assets/checkpoints/pi0_aloha_towel
$\pi_0$-ALOHA-tupperware	Inference	$\pi_0$ model fine-tuned on internal ALOHA data: can unpack food from a tupperware container	gs://openpi-assets/checkpoints/pi0_aloha_tupperware
$\pi_0$-ALOHA-pen-uncap	Inference	$\pi_0$ model fine-tuned on public ALOHA data: can uncap a pen	gs://openpi-assets/checkpoints/pi0_aloha_pen_uncap
$\pi_{0.5}$-LIBERO	Inference	$\pi_{0.5}$ model fine-tuned for the LIBERO benchmark: gets state-of-the-art performance (see LIBERO README)	gs://openpi-assets/checkpoints/pi05_libero
$\pi_{0.5}$-DROID	Inference / Fine-Tuning	$\pi_{0.5}$ model fine-tuned on the DROID dataset with knowledge insulation: fast inference and good language-following	gs://openpi-assets/checkpoints/pi05_droid

By default, checkpoints are automatically downloaded from gs://openpi-assets and are cached in ~/.cache/openpi when needed. You can overwrite the download path by setting the OPENPI_DATA_HOME environment variable.

Running Inference for a Pre-Trained Model

Our pre-trained model checkpoints can be run with a few lines of code (here our $\pi_0$-FAST-DROID model):

from openpi.training import config as _config
from openpi.policies import policy_config
from openpi.shared import download

config = _config.get_config("pi05_droid")
checkpoint_dir = download.maybe_download("gs://openpi-assets/checkpoints/pi05_droid")

# Create a trained policy.
policy = policy_config.create_trained_policy(config, checkpoint_dir)

# Run inference on a dummy example.
example = {
    "observation/exterior_image_1_left": ...,
    "observation/wrist_image_left": ...,
    ...
    "prompt": "pick up the fork"
}
action_chunk = policy.infer(example)["actions"]

You can also test this out in the example notebook.

We provide detailed step-by-step examples for running inference of our pre-trained checkpoints on DROID and ALOHA robots.

Remote Inference: We provide examples and code for running inference of our models remotely: the model can run on a different server and stream actions to the robot via a websocket connection. This makes it easy to use more powerful GPUs off-robot and keep robot and policy environments separate.

Test inference without a robot: We provide a script for testing inference without a robot. This script will generate a random observation and run inference with the model. See here for more details.

Fine-Tuning Base Models on Your Own Data

We will fine-tune the $\pi_{0.5}$ model on the LIBERO dataset as a running example for how to fine-tune a base model on your own data. We will explain three steps: 1. Convert your data to a LeRobot dataset (which we use for training) 2. Defining training configs and running training 3. Spinning up a policy server and running inference

1. Convert your data to a LeRobot dataset

We provide a minimal example script for converting LIBERO data to a LeRobot dataset in examples/libero/convert_libero_data_to_lerobot.py. You can easily modify it to convert your own data! You can download the raw LIBERO dataset from here, and run the script with:

uv run examples/libero/convert_libero_data_to_lerobot.py --data_dir /path/to/your/libero/data

Note: If you just want to fine-tune on LIBERO, you can skip this step, because our LIBERO fine-tuning configs point to a pre-converted LIBERO dataset. This step is merely an example that you can adapt to your own data.

2. Defining training configs and running training

To fine-tune a base model on your own data, you need to define configs for data processing and training. We provide example configs with detailed comments for LIBERO below, which you can modify for your own dataset:

• LiberoInputs and LiberoOutputs: Defines the data mapping from the LIBERO environment to the model and vice versa. Will be used for both, training and inference.
• LeRobotLiberoDataConfig: Defines how to process raw LIBERO data from LeRobot dataset for training.
• TrainConfig: Defines fine-tuning hyperparameters, data config, and weight loader.

We provide example fine-tuning configs for π₀, π₀-FAST, and π₀.₅ on LIBERO data.

Before we can run training, we need to compute the normalization statistics for the training data. Run the script below with the name of your training config:

uv run scripts/compute_norm_stats.py --config-name pi05_libero

Now we can kick off training with the following command (the --overwrite flag is used to overwrite existing checkpoints if you rerun fine-tuning with the same config):

XLA_PYTHON_CLIENT_MEM_FRACTION=0.9 uv run scripts/train.py pi05_libero --exp-name=my_experiment --overwrite

The command will log training progress to the console and save checkpoints to the checkpoints directory. You can also monitor training progress on the Weights & Biases dashboard. For maximally using the GPU memory, set XLA_PYTHON_CLIENT_MEM_FRACTION=0.9 before running training -- this enables JAX to use up to 90% of the GPU memory (vs. the default of 75%).

Note: We provide functionality for reloading normalization statistics for state / action normalization from pre-training. This can be beneficial if you are fine-tuning to a new task on a robot that was part of our pre-training mixture. For more details on how to reload normalization statistics, see the norm_stats.md file.

3. Spinning up a policy server and running inference

Once training is complete, we can run inference by spinning up a policy server and then querying it from a LIBERO evaluation script. Launching a model server is easy (we use the checkpoint for iteration 20,000 for this example, modify as needed):

uv run scripts/serve_policy.py policy:checkpoint --policy.config=pi05_libero --policy.dir=checkpoints/pi05_libero/my_experiment/20000

This will spin up a server that listens on port 8000 and waits for ob