README.md

metadata

title: Pi0.5
emoji: 🔥
colorFrom: red
colorTo: yellow
sdk: gradio
sdk_version: 5.49.1
python_version: '3.10'
app_file: app.py
pinned: false

π₀.₅ UR Action Predictor

This Hugging Face Space deploys a π₀.₅ policy trained on the local UR LeRobot dataset. It predicts an action chunk for inspection or download; it never connects to or commands a robot.

The Policy config selector supports:

• pi05_ur_demo_no_state (default): no discrete robot-state conditioning;
• pi05_ur_demo_state: uses the seven current-state values as discrete state conditioning.

The seven state controls remain visible in both modes so configurations can be switched without rebuilding the page. The repository name does not select the configuration automatically, and changing the selection reloads the policy.

Model repository

Configure these Space variables (or enter both values in the UI):

• PI05_MODEL_ID: Hugging Face repository containing the trained checkpoint.
• PI05_CHECKPOINT_PATH: relative checkpoint directory, for example checkpoints/30000.

The selected directory must contain either params/ (JAX checkpoint) or model.safetensors (PyTorch checkpoint), plus the training statistics at:

assets/**/norm_stats.json

The Space also accepts a root-level norm_stats.json. The asset directory name is not required to be ur_demo; this supports checkpoints exported with the original dataset or robot name, such as assets/F-Fer/ur-1/norm_stats.json.

Use a Space secret named HF_TOKEN when the model repository is private.

Inputs and outputs

The two image inputs correspond to training fields video.image_0 (fixed camera) and video.wrist (wrist camera). State values must use this exact order:

x, y, z, roll, pitch, yaw, gripper

The policy returns ten actions with columns:

dx, dy, dz, droll, dpitch, dyaw, gripper

All state values must be finite. TCP translation uses metres and rotation uses radians, matching the collected dataset.

Deploy

Create a Hugging Face Gradio Space with a CUDA GPU and push this repository. When PI05_MODEL_ID and PI05_CHECKPOINT_PATH are configured as Space variables, the checkpoint is downloaded and validated during app startup, before the GPU-decorated prediction call. Model initialization remains lazy on the first prediction. If those variables are left empty, download falls back to the first prediction. Only one inference request runs at a time to protect GPU memory.

For local use with all dependencies installed:

PYTHONPATH=openpi_runtime python app.py

The Space runs on Python 3.10 and requires a compatible CUDA 12 GPU.

Optional real-checkpoint smoke test

After installing the dependencies on a CUDA machine, opt in to the large model download and end-to-end inference test with:

PI05_GPU_SMOKE=1 \
PI05_MODEL_ID=owner/model \
PI05_CHECKPOINT_PATH=checkpoints/30000 \
pytest tests/test_gpu_smoke.py -v

Do not put a Hugging Face access token in this command; use HF_TOKEN as a local environment secret or a Hugging Face Space secret.

哈基米