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+`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. diff --git a/load_policy.py b/load_policy.py new file mode 100644 index 0000000000000000000000000000000000000000..1cb7fb0f070f4d8808fdb79ddfbca1a1ade426a7 --- /dev/null +++ b/load_policy.py @@ -0,0 +1,38 @@ +"""Load the released RoboTwin Pi05 checkpoint with portable asset paths.""" + +from pathlib import Path + +from openpi.models.pi0_config import Pi0Config +from openpi.policies.policy_config import create_trained_policy +from openpi.training.config import AssetsConfig, DataConfig, LeRobotAlohaDataConfig, TrainConfig + +ASSET_ID = "RoboTwin-pi05_900-aloha_agilex-joint" +CONFIG_NAME = "pi05_base_aloha_full_sim_robotwin_900_defaults" + + +def make_config(checkpoint_dir): + checkpoint_dir = Path(checkpoint_dir).resolve() + return TrainConfig( + name=CONFIG_NAME, + model=Pi0Config(pi05=True), + data=LeRobotAlohaDataConfig( + repo_id=ASSET_ID, + assets=AssetsConfig( + assets_dir=str(checkpoint_dir / "assets"), + asset_id=ASSET_ID, + ), + base_config=DataConfig(prompt_from_task=True), + use_delta_joint_actions=True, + adapt_to_pi=False, + ), + ) + + +def load_policy(checkpoint_dir="14999"): + checkpoint_dir = Path(checkpoint_dir).resolve() + if not (checkpoint_dir / "params").is_dir(): + raise FileNotFoundError(f"Missing parameters: {checkpoint_dir / 'params'}") + stats = checkpoint_dir / "assets" / ASSET_ID / "norm_stats.json" + if not stats.is_file(): + raise FileNotFoundError(f"Missing normalization statistics: {stats}") + return create_trained_policy(make_config(checkpoint_dir), str(checkpoint_dir)) diff --git a/openpi_inference_source.tar.gz b/openpi_inference_source.tar.gz new file mode 100644 index 0000000000000000000000000000000000000000..aa77a5da4f5d391456502cc79c3a3363d9810d0f --- /dev/null +++ b/openpi_inference_source.tar.gz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3620dbe6d300aa16ece80b74ab29236bc166f6bdfe733675674e9ec28f3b6b32 +size 329404