Pi0 Retail
Model source
- Kind
- Other
- File size
- 7.36 KB–81.1 MB
- Updated
- Sep 14, 2026
- Publisher
- @JianwenCao
Source excerpt
Model artifacts
2 artifacts
Model artifacts
2 artifactsadapter_model.safetensors
safetensors · 81.1 MB · SHA-256 71426df4f659…aad7
policy_preprocessor_step_5_normalizer_processor.safetensors
safetensors · 7.36 KB · SHA-256 2b0a5f9301a4…b1c1
Source excerpts
3 excerpts
Source excerpts
3 excerpts--- datasets: JianwenCao/retail library_name: lerobot license: apache-2.0 model_name: pi0 pipeline_tag: robotics tags: - robotics - pi0 - lerobot --- # Model Card for pi0 <!-- Provide a quick summary of what the model is/does. --> **π₀ (Pi0)** π₀ is a Vision-Language-Action model for general robot control, from Physical Intelligence. The LeRobot implementation is adapted from their open source OpenPI repository. **Model Overview** π₀ represents a breakthrough in robotics as the first general-purpose robot foundation model developed by Physical Intelligence. Unlike traditional robots that are narrow specialists programmed for repetitive motions, π₀ is designed to be a generalist policy that can understand visual inputs, interpret natural language instructions, and control a variety of different robots across diverse tasks. For more details, see the [Physical Intelligence π₀ blog post](https://www.physicalintelligence.company/blog/pi0). This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot). See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index). --- ## How to Get Started with the Model For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy). Below is the short version on how to train and run inference/eval: ### Train from scratch ```bash lerobot-train \ --dataset.repo_id=${HF_USER}/<dataset> \ --policy.type=act \ --output_dir=outputs/train/<desired_policy_repo_id> \ --job_name=lerobot_training \ --policy.device=cuda \ --policy.repo_id=${HF_USER}/<desired_policy_repo_id> --wandb.enable=true ``` _Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._ ### Evaluate the policy/run inference ```bash lerobot-record \ --robot.type=so100_follower \ --dataset.repo_id=<hf_user>/eval_<dataset> \ --policy.path=<hf_user>/<desired_policy_repo_id> \ --episodes=10 ``` Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint. --- ## Model Details - **License:** apache-2.0
Source context: 3 downloads · 0 likes · Pipeline robotics · Library lerobot · Repo JianwenCao/pi0_retail
Source context: 0 downloads · 0 likes · Pipeline robotics · Library lerobot · Repo JianwenCao/pi0_retail
