Fine-tune and serve Physical Intelligence OpenPI models (pi0, pi0-fast, pi0.5) using JAX or PyTorch backends for robot policy inference across ALOHA, DROID, and LIBERO environments. Use when adapting pi0 models to cus…
---
name: fine-tuning-serving-openpi
description: Fine-tune and serve Physical Intelligence OpenPI models (pi0, pi0-fast, pi0.5) using JAX or PyTorch backends for robot policy inference across ALOHA, DROID, and LIBERO environments. Use when adapting pi0 models to custom datasets, converting JAX checkpoints to PyTorch, running policy inference servers, or debugging norm stats and GPU memory issues.
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [OpenPI, Physical Intelligence, VLA, Robotics, JAX, PyTorch, Fine-Tuning, Policy Serving, ALOHA, DROID, LIBERO, pi0]
dependencies: [uv>=0.4.0, jax>=0.4.30, torch>=2.1.0, transformers>=4.53.2]
---
# OpenPI Fine-Tuning and Serving
End-to-end workflows for fine-tuning and serving Physical Intelligence's OpenPI models (pi0, pi0-fast, pi0.5) on robot manipulation tasks from the public `openpi` repository. Covers blank-machine setup, JAX training, PyTorch training, checkpoint conversion, and policy inference serving.
## Quick start
Clone the public repo, install the workspace, then serve a pretrained policy:
```bash
git clone --recurse-submodules https://github.com/Physical-Intelligence/openpi.git
cd openpi
GIT_LFS_SKIP_SMUDGE=1 uv sync
GIT_LFS_SKIP_SMUDGE=1 uv pip install -e .
uv run scripts/serve_policy.py --env DROID
```… load the full skill through Skill Me