| --- |
| library_name: lerobot |
| base_model: lerobot/pi05_base |
| datasets: |
| - CoRL2026-CSI/SO101-Teleop-Open_drawer_100epi |
| tags: |
| - lerobot |
| - robotics |
| - robot-learning |
| - imitation-learning |
| - behavior-cloning |
| - visuomotor-policy |
| - vision-language-action |
| - pi05 |
| - pi0.5 |
| - openpi |
| - paligemma |
| - so101 |
| - teleoperation |
| - open-drawer |
| - pytorch |
| --- |
| |
| # Pi0.5 Teleop Open Drawer |
|
|
| This repository contains a LeRobot Pi0.5 policy fine-tuned for the SO101 |
| `open_drawer` teleoperation task. |
|
|
| The checkpoint was fine-tuned from `lerobot/pi05_base` on |
| `CoRL2026-CSI/SO101-Teleop-Open_drawer_100epi` and saved after the final |
| training step. |
|
|
| ## Model Details |
|
|
| - **Policy type:** `pi05` |
| - **Base model:** `lerobot/pi05_base` |
| - **Training dataset:** `CoRL2026-CSI/SO101-Teleop-Open_drawer_100epi` |
| - **Task:** open a drawer with SO101 teleoperation demonstrations |
| - **Checkpoint:** final checkpoint at step `2200` |
| - **Action dimension:** `6` |
| - **State dimension:** `32` |
| - **Image resolution:** `224 x 224` |
| - **Precision:** `bfloat16` |
| - **Training framework:** LeRobot |
|
|
| ## Input and Output Features |
|
|
| The policy checkpoint is configured with the following observation features: |
|
|
| - `observation.images.base_0_rgb`: visual input, shape `[3, 224, 224]` |
| - `observation.images.left_wrist_0_rgb`: visual input, shape `[3, 224, 224]` |
| - `observation.images.right_wrist_0_rgb`: visual input, shape `[3, 224, 224]` |
| - `observation.state`: robot state, shape `[32]` |
|
|
| The output feature is: |
|
|
| - `action`: robot action, shape `[6]` |
|
|
| The saved policy preprocessor maps dataset camera keys as follows: |
|
|
| - `observation.images.top` -> `observation.images.base_0_rgb` |
| - `observation.images.left_wrist` -> `observation.images.left_wrist_0_rgb` |
|
|
| ## Training |
|
|
| Training used the following main settings: |
|
|
| - **Steps:** `2200` |
| - **Batch size:** `32` |
| - **Gradient accumulation:** `4` |
| - **Optimizer:** AdamW |
| - **Learning rate:** `2.5e-5` |
| - **Scheduler:** cosine decay with warmup |
| - **Image augmentation:** enabled |
| - **Final training loss:** `0.0379133597` |
| - **Final train steps logged:** `2200` |
| - **Final train samples logged:** `140800` |
| - **Final train epochs logged:** `6.2694808086` |
|
|
| No separate evaluation results are included in this repository. |
|
|
| ## Usage |
|
|
| Use the model as a LeRobot policy by pointing `--policy.path` at this Hub repo: |
|
|
| ```bash |
| lerobot-record \ |
| --robot.type=<your_robot_type> \ |
| --dataset.repo_id=<your_eval_dataset_repo> \ |
| --policy.path=CoRL2026-CSI/pi05_teleop_open_drawer \ |
| --episodes=10 |
| ``` |
|
|
| You can also load it directly in Python: |
|
|
| ```python |
| from lerobot.policies.pi05.modeling_pi05 import PI05Policy |
| |
| policy = PI05Policy.from_pretrained("CoRL2026-CSI/pi05_teleop_open_drawer") |
| policy.eval() |
| ``` |
|
|
| ## Files |
|
|
| - `model.safetensors`: policy weights |
| - `config.json`: Pi0.5 policy configuration |
| - `train_config.json`: training configuration |
| - `policy_preprocessor.json`: saved policy input processor pipeline |
| - `policy_postprocessor.json`: saved policy output processor pipeline |
| - `*_processor.safetensors`: normalization and unnormalization state |
|
|
