fine-tuning-serving-openpi
Category: multimodal-and-emerging · License: MIT · Version: 1.0.0
Tags: OpenPI, Physical Intelligence, VLA, Robotics, JAX, PyTorch, Fine-Tuning, Policy Serving, ALOHA, DROID, LIBERO, pi0
When to use it
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.Example requests
Ask your agent in plain language, for example:- “How do I serve the pi0.5 DROID policy on one GPU so my robot can query it?”
- “I’m fine-tuning on my own LeRobot dataset, hf repo my-lab/ur5_pick_place, starting from pi05_libero in openpi’s config.py. I launched train.py and it died with a missing norm stats error. After I fixed that, JAX OOM’d on my 1x A100 80GB. What should I do?”
- “My lab’s vision-language-action model checkpoint was trained in JAX, but our deployment stack only supports PyTorch and we want to train further with multiple GPUs. How do we get there?”
Install
/plugin marketplace add KalarisLabs/research-agent-skills and install the whole multimodal-and-emerging category with /plugin install multimodal-and-emerging@research-agent-skills.