| --- |
| license: apache-2.0 |
| library_name: lerobot |
| pipeline_tag: robotics |
| tags: |
| - robotics |
| - lerobot |
| - pi05 |
| - vla |
| - bimanual |
| - deformable-object-manipulation |
| - cloth |
| --- |
| |
| # π0.5 (pi05_evo) — flatten_tshirt (bimanual cloth flattening) |
|
|
| A π0.5 vision-language-action policy fine-tuned on the `flatten_tshirt` task of a bimanual |
| deformable-object (cloth / bag) manipulation benchmark. The robot is a dual-arm Piper; the |
| task is to flatten a crumpled t-shirt on a table. |
|
|
| Simulation uses a GPU cloth solver co-simulated with the robot in a single model, and |
| observations are rendered with a photorealistic renderer. |
|
|
| ## Model |
|
|
| | | | |
| |---|---| |
| | Architecture | `pi05_evo` (LeRobot), fine-tuned from `lerobot/pi05_base` | |
| | Precision | bfloat16 | |
| | Observation | 3 × RGB (`static_cam`, `left_hand_cam`, `right_hand_cam`) + 14-D joint state | |
| | Action | 14-D (left 6 joints + gripper, right 6 joints + gripper) | |
| | Chunk size / action steps | 50 / 50 | |
| | `max_state_dim` / `max_action_dim` | 32 / 32 (14-D state and action are padded) | |
|
|
| ## Training |
|
|
| | | | |
| |---|---| |
| | Dataset | `flatten_tshirt_200` — 200 episodes / 41,464 frames, LeRobot v3.0, 25 fps | |
| | Steps | 30,000 | |
| | Batch size | 8 (single A100-80G, gradient checkpointing) | |
| | Learning rate | 2.5e-5 | |
| | Seed | 1000 | |
| | Image augmentation | enabled, max 3 random transforms per sample | |
|
|
| Augmentation follows a tuned recipe (brightness / contrast / saturation / hue / sharpness / |
| small affine) rather than the LeRobot default, which is disabled. The brightness range is |
| deliberately asymmetric toward the darker side: simulation lighting is idealized while real |
| RealSense D435i footage tends to be darker, so biasing augmentation toward darker samples is |
| the right direction for sim-to-real transfer. |
|
|
| ### Reproducing: pin the base model revision |
|
|
| This checkpoint was fine-tuned from `lerobot/pi05_base` at revision |
| `9e55186ad36e66b95cda57bc47818d9e6237ae30`. **Pin that revision** if you fine-tune from base |
| yourself — a later snapshot of that repo ships a `policy_preprocessor.json` containing a |
| `relative_actions_processor` that is not registered in this version of LeRobot and fails to |
| load. This only affects training from base; loading *this* checkpoint is unaffected. |
|
|
| ## Status |
|
|
| ⚠️ **This checkpoint has not yet been formally evaluated.** It has only been through a |
| small smoke-level closed-loop run, not the benchmark's standard N=100 protocol. Success-rate |
| numbers are deliberately not published here yet; they will be added once the full evaluation |
| has been run. Treat this as a training artifact, not a reported result. |
|
|
| ## Usage |
|
|
| ```python |
| from lerobot.policies.pi05_evo.modeling_pi05_evo import PI05EvoPolicy |
| |
| policy = PI05EvoPolicy.from_pretrained("deformable-bench/pi05-flatten-tshirt") |
| ``` |
|
|
| The policy expects the three camera streams named exactly as listed above, plus a 14-D |
| `observation.state`, and returns a 14-D action. |
|
|
| **Use the model's default inference parameters.** Overriding `n_action_steps` in particular |
| has been observed to degrade closed-loop success substantially on this benchmark. |
|
|
| ## License |
|
|
| Apache-2.0. |
|
|
