| --- |
| language: |
| - en |
| - zh |
| tags: |
| - robotics |
| - pi05 |
| - openpi |
| - robotwin |
| - aloha-agilex |
| --- |
| |
| # Pi05 fine-tuned on RoboTwin (900 demonstrations) |
|
|
| This repository contains the inference checkpoint from a Pi05 fine-tuning run on |
| 900 RoboTwin Aloha AgileX demonstrations (159,568 frames), covering |
| `place_object_basket`, `place_object_scale`, and `place_object_stand`. |
| Training completed 15,000 steps on October 7, 2026 (UTC+08:00). |
| The final checkpoint is `14999`, because steps are indexed from zero. |
| The last logged training loss was 0.0013 at step 14900. No evaluation success |
| rate is reported here; training loss does not establish task success. |
|
|
| ## Files |
|
|
| - `14999/params/`: JAX/Orbax model parameters (approximately 12 GiB). |
| - `14999/assets/`: normalization statistics required for inference. |
| - `14999/_CHECKPOINT_METADATA`: original checkpoint metadata. |
| - `load_policy.py`: portable inference configuration and loading helper. |
| - `openpi_inference_source.tar.gz`: snapshot of the local OpenPI source, |
| client package, dependency declarations, lockfile, and upstream code license. |
|
|
| Optimizer/training state and the demonstration dataset are not included. |
| This is a JAX/Orbax checkpoint, not a Transformers `from_pretrained` model. |
| The model repository hosts downloadable files, not a hosted inference endpoint. |
|
|
| ## Download and install |
|
|
| ```bash |
| python -m pip install -U huggingface_hub |
| hf download renbin1/pi05 --local-dir pi05 |
| cd pi05 |
| mkdir -p openpi_runtime |
| tar -xzf openpi_inference_source.tar.gz -C openpi_runtime |
| cd openpi_runtime |
| uv sync --frozen --no-dev |
| uv pip install huggingface_hub |
| cd .. |
| ``` |
|
|
| Install `uv` first if it is not already available. Use Python 3.11 and a |
| CUDA-compatible NVIDIA GPU environment. The training environment used |
| JAX/JAXlib 0.5.3, Flax 0.10.2, Orbax Checkpoint 0.11.13, NumPy 1.26.4, |
| and Torch 2.10.0. The bundled lockfile supplies the remaining dependencies. |
| First use may download OpenPI tokenizer assets. |
|
|
| ## Run inference |
|
|
| Run the following Python code using `openpi_runtime/.venv/bin/python`, from the |
| downloaded repository directory: |
|
|
| ```python |
| import numpy as np |
| from load_policy import load_policy |
| |
| policy = load_policy("14999") |
| observation = { |
| "state": state.astype(np.float32), # real robot state, shape (14,) |
| "images": { |
| "cam_high": head_rgb, |
| "cam_left_wrist": left_wrist_rgb, |
| "cam_right_wrist": right_wrist_rgb, |
| }, |
| "prompt": instruction, |
| } |
| result = policy.infer(observation) |
| actions = result["actions"] # shape (50, 14), joint/gripper action sequence |
| ``` |
|
|
| Supply actual observations: `state`, images, and `instruction` are placeholders. |
| Images must be RGB uint8 arrays in HWC or CHW layout. Training images were |
| 480x832 and the model transform resizes them to 224x224. |
| The 14 state/action dimensions are ordered as left arm joints (6), left gripper |
| (1), right arm joints (6), right gripper (1). Match the RoboTwin Aloha AgileX |
| joint conventions. The loader preserves joint delta transforms internally and |
| returns absolute actions; grippers remain absolute. `adapt_to_pi=False` matches |
| training. Inference does not require the training dataset. |
|
|
| ## Configuration and provenance |
|
|
| Training configuration: `pi05_base_aloha_full_sim_robotwin_900_defaults`. |
| Normalization asset ID: `RoboTwin-pi05_900-aloha_agilex-joint`. |
| Architecture: `Pi0Config(pi05=True)`, action horizon 50, internal action width 32, |
| with 14 robot action dimensions returned by the Aloha output transform. |
| The source snapshot includes the custom training configuration. Its git base |
| was XPolicyLab commit `9c98a3aaf02d05c6f9999a5a0a7a42090555ddf3`; local changes |
| are captured in the snapshot, so the commit alone is not the complete source. |
| The portable helper loads normalization from the downloaded checkpoint and |
| does not depend on paths from the training machine. |
|
|
| Upstream projects: [OpenPI](https://github.com/Physical-Intelligence/openpi), |
| [XPolicyLab](https://github.com/XPolicyLab/XPolicyLab), and |
| [RoboTwin](https://github.com/RoboTwin-Platform/RoboTwin). |
| The code license is included in the source archive. This model card does not |
| assign a new license to the weights; consult the applicable base-model and |
| dataset terms before reuse or redistribution. |
|
|
| Validation for this release checks file presence, normalization dimensions, |
| and configuration construction. GPU inference and task evaluation have not |
| been validated as part of the upload preparation. |
|
|
