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.
[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:
bash
1git clone --recurse-submodules git@github.com:Physical-Intelligence/openpi.git
23# Or if you already cloned the repo:4git 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:
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.
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
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):
python
1from openpi.training import config as _config
2from openpi.policies import policy_config
3from openpi.shared import download
45config = _config.get_config("pi05_droid")6checkpoint_dir = download.maybe_download("gs://openpi-assets/checkpoints/pi05_droid")78# Create a trained policy.9policy = policy_config.create_trained_policy(config, checkpoint_dir)1011# Run inference on a dummy example.12example ={13"observation/exterior_image_1_left":...,14"observation/wrist_image_left":...,15...16"prompt":"pick up the fork"17}18action_chunk = policy.infer(example)["actions"]
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:
Convert your data to a LeRobot dataset (which we use for training)
Defining training configs and running training
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 observations to be sent to it. We can then run an evaluation script (or robot runtime) that queries the server.
For running the LIBERO eval in particular, we provide (and recommend using) a Dockerized workflow that handles both the policy server and the evaluation script together. See the LIBERO README for more details.
If you want to embed a policy server call in your own robot runtime, we have a minimal example of how to do so in the remote inference docs.
More Examples
We provide more examples for how to fine-tune and run inference with our models on the ALOHA platform in the following READMEs:
openpi now provides PyTorch implementations of π₀ and π₀.₅ models alongside the original JAX versions! The PyTorch implementation has been validated on the LIBERO benchmark (both inference and finetuning). A few features are currently not supported (this may change in the future):
The π₀-FAST model
Mixed precision training
FSDP (fully-sharded data parallelism) training
LoRA (low-rank adaptation) training
EMA (exponential moving average) weights during training
Setup
Make sure that you have the latest version of all dependencies installed: uv sync
Double check that you have transformers 4.53.2 installed: uv pip show transformers
This overwrites several files in the transformers library with necessary model changes: 1) supporting AdaRMS, 2) correctly controlling the precision of activations, and 3) allowing the KV cache to be used without being updated.
WARNING: With the default uv link mode (hardlink), this will permanently affect the transformers library in your uv cache, meaning the changes will survive reinstallations of transformers and could even propagate to other projects that use transformers. To fully undo this operation, you must run uv cache clean transformers.
Converting JAX Models to PyTorch
To convert a JAX model checkpoint to PyTorch format:
Specify the converted PyTorch model path in your config using pytorch_weight_path
Launch training using one of these modes:
bash
1# Single GPU training:2uv run scripts/train_pytorch.py <config_name> --exp_name <run_name> --save_interval <interval>34# Example:5uv run scripts/train_pytorch.py debug --exp_name pytorch_test
6uv run scripts/train_pytorch.py debug --exp_name pytorch_test --resume # Resume from latest checkpoint78# Multi-GPU training (single node):9uv run torchrun --standalone --nnodes=1 --nproc_per_node=<num_gpus> scripts/train_pytorch.py <config_name> --exp_name <run_name>1011# Example:12uv run torchrun --standalone --nnodes=1 --nproc_per_node=2 scripts/train_pytorch.py pi0_aloha_sim --exp_name pytorch_ddp_test
13uv run torchrun --standalone --nnodes=1 --nproc_per_node=2 scripts/train_pytorch.py pi0_aloha_sim --exp_name pytorch_ddp_test --resume
1415# Multi-Node Training:16uv run torchrun \17 --nnodes=<num_nodes>\18 --nproc_per_node=<gpus_per_node>\19 --node_rank=<rank_of_node>\20 --master_addr=<master_ip>\21 --master_port=<port>\22 scripts/train_pytorch.py <config_name> --exp_name=<run_name> --save_interval <interval>
Precision Settings
JAX and PyTorch implementations handle precision as follows:
JAX:
Inference: most weights and computations in bfloat16, with a few computations in float32 for stability
Training: defaults to mixed precision: weights and gradients in float32, (most) activations and computations in bfloat16. You can change to full float32 training by setting dtype to float32 in the config.
PyTorch:
Inference: matches JAX -- most weights and computations in bfloat16, with a few weights converted to float32 for stability
Training: supports either full bfloat16 (default) or full float32. You can change it by setting pytorch_training_precision in the config. bfloat16 uses less memory but exhibits higher losses compared to float32. Mixed precision is not yet supported.
With torch.compile, inference speed is comparable between JAX and PyTorch.
Troubleshooting
We will collect common issues and their solutions here. If you encounter an issue, please check here first. If you can't find a solution, please file an issue on the repo (see here for guidelines).
Issue
Resolution
uv sync fails with dependency conflicts
Try removing the virtual environment directory (rm -rf .venv) and running uv sync again. If issues persist, check that you have the latest version of uv installed (uv self update).
Training runs out of GPU memory
Make sure you set XLA_PYTHON_CLIENT_MEM_FRACTION=0.9 (or higher) before running training to allow JAX to use more GPU memory. You can also use --fsdp-devices <n> where <n> is your number of GPUs, to enable fully-sharded data parallelism, which reduces memory usage in exchange for slower training (the amount of slowdown depends on your particular setup). If you are still running out of memory, you may way to consider disabling EMA.
Policy server connection errors
Check that the server is running and listening on the expected port. Verify network connectivity and firewall settings between client and server.
Missing norm stats error when training
Run scripts/compute_norm_stats.py with your config name before starting training.
Dataset download fails
Check your internet connection. For HuggingFace datasets, ensure you're logged in (huggingface-cli login).
CUDA/GPU errors
Verify NVIDIA drivers are installed correctly. For Docker, ensure nvidia-container-toolkit is installed. Check GPU compatibility. You do NOT need CUDA libraries installed at a system level --- they will be installed via uv. You may even want to try uninstalling system CUDA libraries if you run into CUDA issues, since system libraries can sometimes cause conflicts.
Import errors when running examples
Make sure you've installed all dependencies with uv sync. Some examples may have additional requirements listed in their READMEs.
Action dimensions mismatch
Verify your data processing transforms match the expected input/output dimensions of your robot. Check the action space definitions in your policy classes.
Diverging training loss
Check the q01, q99, and std values in norm_stats.json for your dataset. Certain dimensions that are rarely used can end up with very small q01, q99, or std values, leading to huge states and actions after normalization. You can manually adjust the norm stats as a workaround.
