from fla.policies import policy_config
from fla.training import config

# Load the policy
config = policy_config.get_policy_config("pi06_koch_tshirt")
policy = config.create_policy()

# Run inference
action = policy(observation)

With FLA Server

bash

# Start server
uv run scripts/serve_policy.py --env aloha_sim \
    policy:checkpoint --checkpoint_path hf://djkesu/pi06-koch-tshirt-5k

# In your robot controller
from openpi_client import OpenPIClient
client = OpenPIClient("http://localhost:8000")
action = client.infer(observation)

Training Details

Base: Pi0.5 pretrained checkpoint
Config: pi06_koch_tshirt
Checkpoint: 5,000 steps (training can be resumed)
Hardware: Lambda / H100

Benchmarks

Task	Notes
Koch t-shirt folding	Task-specific fine-tuning; checkpoint at 5k steps.

Citation

bibtex

@article{pi0,
  title={$\pi_0$: A Vision-Language-Action Flow Model for General Robot Control},
  author={Physical Intelligence},
  year={2024},
}

License

Apache 2.0