deformable-bench/pi05-flatten-tshirt

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

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.