| --- |
| language: |
| - en |
| library_name: lerobot |
| pipeline_tag: robotics |
| tags: |
| - vision-language-action |
| - imitation-learning |
| - lerobot |
| inference: false |
| license: gemma |
| base_model: lerobot/pi05_base |
| --- |
| |
| # pi05_subtask base checkpoint |
| |
| A derived copy of [`lerobot/pi05_base`](https://huggingface.co/lerobot/pi05_base) prepared for the |
| [`lerobot_policy_pi05_subtask`](https://github.com/) LeRobot plugin (π0.5 with System-1 subtask |
| generation, following [openpi_subtask](https://github.com/Ke-Wang1017/openpi_subtask)). |
| |
| **The model weights are bit-identical to `lerobot/pi05_base`.** Only two metadata files differ: |
| |
| - `config.json`: policy `type` is `pi05_subtask` instead of `pi05`. |
| - `policy_preprocessor.json`: pi05's `pi05_prepare_state_tokenizer_processor_step` + |
| `tokenizer_processor` steps are replaced by `pi05_subtask_context_tokenize_processor_step`, |
| which tokenizes the context segment of the two-segment prompt |
|
|
| ``` |
| context (no loss): "Task: {task}.; State: {256-bin ints}; Subtask: " |
| predicted (CE loss): "{subtask}.;\nAction: " + EOS |
| ``` |
|
|
| into `observation.language.context_tokens`/`.context_mask`. The predicted segment is model |
| territory: the policy appends it at training time (computing the CE region mask next to the |
| loss) and decodes it autoregressively at inference. |
|
|
| This exists because LeRobot instantiates processor pipelines from the checkpoint's |
| `policy_preprocessor.json` (by processor-step registry name), so fine-tuning the subtask policy |
| directly from `lerobot/pi05_base` would silently install plain pi05's tokenizer steps. |
|
|
| The same artifact is the base for the `framepick` policy type (`lerobot_policy_framepick`): |
| framepick has no processor steps of its own — frame selection, chunk re-expression and the |
| assignment prefix all live in the policy, configured from the policy config. |
|
|
| ## Usage |
|
|
| Requires the `lerobot_policy_pi05_subtask` plugin installed (LeRobot auto-imports |
| `lerobot_policy_*` distributions). In a training config: |
|
|
| ```json |
| "policy": { |
| "type": "pi05_subtask", |
| "pretrained_path": "dgrachev/pi05_subtask_base", |
| ... |
| } |
| ``` |
|
|
| During training the subtask string is read from `complementary_data["subtask"]` when the dataset |
| provides one, and falls back to the task string otherwise. |
|
|
| Regenerate with `scripts/prepare_base_checkpoint.py` from the plugin repository. |
|
|
