ACT — flatten_tshirt (bimanual cloth flattening)
An ACT policy trained 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 | ACT, ResNet-18 vision backbone |
| Observation | 3 × RGB 720×1280 (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 | 100 / 100 |
n_obs_steps | 1 |
Training
| Dataset | flatten_tshirt_200 — 200 episodes / 41,464 frames, LeRobot v3.0, 25 fps |
| Steps | 30,000 |
| Batch size | 16 (single A100-80G) |
| Learning rate | 1e-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 the augmentation toward darker samples is the right direction for sim-to-real transfer.
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.act.modeling_act import ACTPolicy
policy = ACTPolicy.from_pretrained("hwk0809/act-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. It runs in-process (no policy server needed).
License
Apache-2.0.